The disclosed system and method provide for the automatic assessment of the presence/severity of the sleep problem and its exact nature. The assessment is based on qualitative information about sleep patterns, insomnia-related factors and daytime consequences, as well as quantitative information about sleep patterns measured by a sensor. By combining the different sources of information (subjective as well as objective data), the diagnosis gives more insight into the nature of the sleep problem and is therefore more accurate. Furthermore, the disclosed system may be used to select specific components of the system that are medically relevant to the individual and therefore create a personalized program. The system teaches a selection of self-management skills that could help the individual to better cope with sleep disturbances and target those factors that maintain the problem or make it worse by a particular individual.

Sleep disorders are common. At least 10% of the population suffers from a sleep disorder that is clinically significant and of public health importance. Insomnia is by far the most common form of sleep disturbance.
Most insomnia definitions include descriptions of sleep-specific symptoms with associated daytime complaints. Sleep symptoms typically include a difficulty initiating sleep, difficulty maintaining sleep, final awakening that occurs much earlier than desired or sleep that is non-restorative or of generally poor quality. Waking symptoms are associated with daytime complaints related to fatigue, sleepiness, mood disturbance, cognitive difficulties and social or occupational impairment.
The prevalence of isolated insomnia symptoms in the general population is approximately 30 to 50%, and approximately 9 to 15% report significant daytime impairments as a result of chronic insomnia problems. In a majority of the patient population, insomnia is treated by medication. The market for sleeping pills is about 4.6 B US$. Other forms of treatment such as sleep restriction therapy are less wide-spread although evidence suggests that they are more effective on the long run than pharmaceutical treatment alone.
A standard diagnostic method for assessing the nature and the severity of the sleep problem, a so-called sleep log or sleep diary, i.e. a questionnaire usually on paper, is used in most cases; also an actigraph can be used as an alternative to a sleep log.
The main drawback of this sleep log is that its accuracy is affected by a subjective bias of the patient, e.g. for patients it is often difficult to remember sleep and wake periods during the night correctly.
An automatic detection of sleep and wake stages requires the measurement of vital body signs, but unfortunately most of the existing solutions rely on adhesive electrodes, e.g. by sticking or gluing them to the patient's skin to wear electrodes, e.g. for EEG on the head. Those electrodes are obtrusive to the patient during sleeping due to the cables and recording devices, which are connected to those sensors. Additionally, there is a problem that during resting time, cables or electrodes might loose the connection, which lowers the quality of the received signal.
The diagnosis of insomnia with an automatic sleep log in a fully equipped sleep laboratory is expensive, places are not available over long time duration and thus ability for diagnosis for insomnia patients are limited.
Further, additional sensors and cabling along the patient may disturb the patient's sleep, and therefore influence the evaluation of sleep and wake stages during the night time, which leads to an influence on the sleep records and might lead to a wrong diagnosis.
U.S. Patent Application Publication No. 2004/0225179 discloses automated behavioral methods and systems for treating insomnia that use passive means for determining wake/sleep states and is incorporated herein by reference.
Insomnia is a prevalent sleep disturbance in the general population. However, in practice, patients tend to not seek treatment or are diagnosed by a non sleep specialist, e.g. a GP, without the use of a standard and proper method for assessing the nature and the severity of the sleep problem. This usually results in the underestimation of the reality of the sleep problem and/or the prescription of sleeping pills, although a drug-free therapy—the cognitive behavioural therapy for insomnia (CBT-I)—has been shown to be as effective and more sustainable. Further, a more accurate assessment and a tailored therapy may lead to a higher compliance and consequently to a better medical outcome.
An object of the disclosed concept is to provide a system structured to facilitate cognitive behavioral therapy for a patient having insomnia, the system including a communication assembly structured to provide for electronic communication, a sensor system having at least one sensor, the sensor system structured to detect sleeping activity data and to provide a sensor system signal incorporating the sleeping activity data, the sensor system coupled to, and in electronic communication with, the communication assembly, a first processing unit coupled to, and in electronic communication with, the communication assembly, the first processing unit structured to receive the sensor system signal and to convert the sleeping activity data into sleep pattern data, a second processing unit having an input assembly and structured to gather patient input data, the second processing unit coupled to, and in electronic communication with, the communication assembly, a third processing unit coupled to, and in electronic communication with, the communication assembly, the third processing unit structured to receive the sleep pattern data and the patient input data, perform an analysis thereon thereby creating a patient sleep profile, a fourth processing unit coupled to, and in electronic communication with, the communication assembly, the fourth processing unit structured to analyze the patient sleep profile and to provide a course of therapy related to the patient sleep profile, and a display coupled to, and in electronic communication with, the communication assembly, the display structured to present a user interface.
A further object of the disclosed concept is to provide a method of providing cognitive behavioral therapy for insomnia, the method including the steps of monitoring a patient's sleep utilizing a sensor system having at least one unobtrusive sensor, the sensor system structured to detect sleeping activity data, gathering patient input data, combining the patient input data and the sleeping activity data to create a patient sleep profile, analyzing a patient's sleep profile to determine a course of therapy, and presenting the course of therapy to the patient on a display.
As used herein, the term "patient" does not only apply on human beings, but also on animals. Further, the term "patient" does not mean that the respective person/animal is disease ridden, thus, also healthy people will be referred to as "patients."
The term "sleep/wake classification" refers to the classification of the epoch of interest as "wake" or "sleep," resulting from the probability of belonging to the respective class, or the classification "true" or "false" given as an output of the classifier but also refers to the display of the classifier results on a user interface or any device.
Additionally, the term "pNN50" refers to the percentage of the number of interval differences of adjacent NN-Intervals greater than 50 ms, the term "SDNN" refers to the standard deviation of all NN-Intervals, the term "SDSD" refers to the Standard deviation between distances between adjacent intervals, the term "RR_mean" refers to mean duration of the RR-Intervals, the term "HR_mean" refers to the mean heart rate, the term "LF" refers to the low frequency range, as defined by heart rate variability standards, the term "HF" refers to the high frequency range, as defined by heart rate variability standards, the term "RMSSD" refers to root mean square successive differences, and the term "HRV" refers to heart rate variability.
As can be seen from FIG. 1, the system for sleep/wake classification 100 according to the preferred embodiment of the present invention comprises unobtrusive in-bed sensors for vital body sign monitoring 101 of the heart activity from the ECG, and/or the body movements from a bed foil sensor or the like as will be described in the following. A further step is the pre-processing unit 102 for filtering and artifact removal during preparation of the signals, a feature extraction unit 103 extracting in particular features from the ECG and/or the body movements signal alone or in combination, then there is a sleep/wake classifier unit 104 for classification of the sleep/wake status according to all the input features, sleep efficiency calculation unit 105 for calculating the time asleep compared to the time in bed, used as input for the patient sleep restriction algorithm, device 106 providing, for example, rules for a healthier sleep to the patient.
The output of the sleep/wake classification 104, sleep efficiency calculation 105 and sleep restriction algorithm device 106 can be used to provide feedback to the patient 107 or to the medical professional, who could also get information from additional sources like the sleep log questions for subjective parameters 109.
As for the sensor part of the proposed system, the following embodiments are possible: The sensor is a ferro-electret foil placed underneath the patient's thorax to measure heart rate, respiration and body movements.
Alternatively, in another preferred embodiment a piezo-resistive strain gauge glued onto a slat underneath the mattress in the patient's thorax region can be used to measure heart rate, respiration and body movements.
In another preferred embodiment only an ECG would be used, preferably a textile ECG integrated as a pillow and foot mat electrode in the bed.
Instead of the ferro-electret foil or the strain gauge, the ECG sensor could be combined with a respiration signal, measured with a standard (inductive or piezo-resistive) band around the thorax and/or the abdomen. This type of sensor can be also integrated into textiles (e.g. a T-shirt) to make it more unobtrusive.
Alternatively, in another embodiment instead of the ferro-electret foil or the strain gauge, the ECG sensor could be combined with an accelerometer signal. The device can be a wrist-worn device, but preferably a 2D or 3D accelerometer is placed on the patient's trunk to measure body movements. Also, this type of sensor can be integrated into textiles (e.g. a T-shirt) to make it more unobtrusive.
The processing unit comprises several steps as can be seen in FIG. 1. These are the preprocessing of the raw data, feature extraction and the sleep/wake classification. In the following paragraphs the different alternatives for each of these steps will be described in more detail.
The signal pre-processing device 102 of FIG. 1 includes one or more of the following steps which can be passed through serially, parallel or repeatedly:
appropriate filtering of the signal(s); and
artifact removal.
In case of an ECG signal, the removal of ectopic beats can be necessary.
In case of gaps in the signal, interpolation might be necessary.
The feature extraction device 103 includes extraction of features from the ECG and a respiration signal comprising:
From the ECG, the following features are derived:
statistical heart rate variability parameters from the time domain (e.g. mean heart rate, SDNN, RMSSD etc.);
parameters from the heart rate variability (HRV) spectrum (e.g. LF, HF);
multi-scale sample entropy; and
progressive detrended fluctuation analysis.
From the respiration signal-measured by the ferro-electret foil, the slat sensor or the inductive/piezo-resistive band—the spectrum is calculated and the LF and HF power are extracted as features. In addition, the mean breathing rate is determined.
Further, the feature extraction device 103 allows in case of both, the ECG signal and a respiration signal are available, to calculate the coherent power of both spectra as an additional feature. Moreover, the ratio of heart rate and breathing rate can be derived as an additional feature.
Furthermore, from the ferro-electret foil or the slat sensor signal, an activity index is derived based on large body movements.
The preferred embodiment of the present invention further suggests that for the next step—the sleep/wake classification 104—the activity index and at least one additional feature relating to the cardiac and/or respiratory status are a preferable combination as input for the classification process.
A vector, of at least one feature, and preferably all or at least a sub-set of the above-mentioned features is generated for each epoch of interest, e.g. each 1-minute segment of data.
Further, this vector is fed into a sleep/wake classifier 104 which is based on a standard pattern recognition approach with supervised learning, as will be described in FIG. 3. For the classifier, the following approaches can preferably be used:
Bayesian linear or quadratic discriminant classifier;
support vector machine;
k-Nearest-Neighbour (kNN) method;
Neural Network; and
Hidden Markov Model.
The parameters of the classifier are trained on a large database of representative data.
In order to receive the patient's input regarding the subjective questions in the sleep log, Insomnia Severity Index, Insomnia Frequency Questionnaire, or other assessments and to give feedback to him, the input unit and the display unit are preferably combined in the preferred embodiment of the present invention in one user interface device 107. This device can be a normal laptop PC, a tablet PC with a touch screen, a handheld device such as a PDA or a mobile phone. Depending on the processing power of this device, the processing unit can also be part of this device. The feedback to the patient can contain one or more of the following parameters: time in bed, total sleep time, total wake time, sleep efficiency, sleep latency, number and durations of awakenings, or a simplified hypnogram.
The feedback to the medical professional can be given via the same user interface 108. In another embodiment, there is a download procedure to transfer the patient's data from the patient user interface to the physician's PC (e.g. via a USB cable, via Bluetooth, ZigBee or any other communication standard or device). In yet another embodiment, the patient's data can be (automatically) sent to the physician via the Internet or GSM, UMTS, EDGE, GPRS, or any other Internet or mobile phone standard or system.
The feedback to the medical professional should contain all of the above-mentioned parameters. Furthermore, it should also contain the patient's answers to the subjective sleep log questions so that he can compare the subjective and objective data which gives him important information for the appropriate therapy approach. This is, for example, especially important in case of sleep-state misperception, a type of insomnia where objective sleep data reflect a normal sleep pattern but the patient himself does not recognize that he has slept.
As can be seen in FIG. 2, the ECG signal provided by an ECG sensor 200 undergoes a pre-processing 201, wherein the preprocessing consists in R-peak detection, ectopic beat removal, linear interpolation and resampling at a predefined frequency, preferably at 4 Hz. The resulting RR-interval series is then considered and heart rate variability parameters, according to the standard, are assessed in frequency and time domains. First the power spectrum calculation 202 over a predefined time period, preferably a 5-minute segment of the time series, centered on the one-minute epoch of interest, is computed using preferably an autoregressive model with advanced detrending. Alternatively, other methods based on Fourier analysis, time-frequency distributions, time-varying autoregressive modeling is available. In the two last cases, the power spectral estimation is updated on a shorter time scale, e.g. every new detected R-peak of the ECG.
The power spectrum in the low-frequency band LF preferably at 0.04-0.15 Hz and in the high frequency band HF preferably at 0.15-0.4 Hz are used to define the spectral features in 203: the power is normalized according to LF_norm=LF/(LF+HF) and LF/HF ratio is calculated. Furthermore, the RR-interval series time statistics over a predefined time period, preferably a 5-minute segment, in 204 provides the resulting time domain features in 205, e.g. pNN50 (percentage of the number of interval differences of adjacent NN-Intervals greater than 50 ms), SDNN (standard deviation of all NN-Intervals), SDSD (standard deviation of successive differences between adjacent intervals), RMSSD (root mean square successive differences), RR_mean (mean duration of the RR-Intervals) and HR_mean (mean of the instantaneous heart rate).
Non-linear parameters are also extracted from the RR-interval time series in 206 by the application of two methods. The first non-linear calculation method used in 207 is progressive detrended fluctuation analysis which allows to gradually integrate the signal before detrending over windows of length 64. Further, the partial sums of the squared signal are then considered and this provides the resulting differentiated time series in 208 from which we extract a new feature 209, defined as the maximum value over the considered epoch, preferably a one-minute epoch but which can be any predefined epoch duration.
The second non-linear calculation method applied to the RR-interval series provides the multiscale sample entropy. Firstly, the series is coarse-grained at scales 1 and 2 in 210 and 5-minute segments are considered. The sample entropy (hereafter called sampen) is calculated in 211 at several levels from 1 to 10 Hence, the following features in 212 are provided: sampen_scale1_k, for levels k=1 to 10 and sampen_scale2_k, for levels k=1 to 10.
As can be seen in the lower part of FIG. 2, the bed foil signal provided by bed foil sensor 213 is undergoing a pre-processing 214 which consists of noise reduction and calibration. The use of low pass filtering 215 leads to the breathing signal. Peak identification on this signal allows to deduce the breath interval series, which is also linearly interpolated and resampled at a predefined rate, preferably at 4 Hz in 216. The power spectrum 217 is computed using preferably an autoregressive model with advanced detrending. The power spectrum is then split and normalized in 218 in the low-frequency band LF (0.04-0.15 Hz) and in the high frequency band HF (0.15-0.4 Hz), which are used to define the spectral features LF_norm_respi and LF/HF ratio_respi. Besides, the detection of small and large energy artefacts in 219 allows to define a heuristic activity index over one-minute epochs, also used as a feature in 220. Further, the band-pass filtering in 221 delivers the so-called ballistocardiogram, which represents the mechanical heart activity. This signal could be an interesting alternative to the ECG signal to get the heart rate variability signal. Finally, the RR-interval and the breath interval series are combined in 222 by estimating the squared coherence function over 5-minute epochs centred on the one-minute epoch of interest. This coherence function is multiplied by the autospectrum of the RR-interval series and integrated along the frequency axis. The resulting feature in 223 is the amount of coherent power in %. We could also think of other features assessing the cardiopulmonary coupling, like the RR-interval/breath interval ratio.
As can be seen in the right part of FIG. 2, a preferred embodiment contains the features 203, 205, 209, 212, 223, 218, 220, which form the components of the feature vector used in the classification process which is further described in FIG. 3.
FIG. 3 is a preferred embodiment of the method for sleep/wake classification. Step 303 represents information coming from the feature extraction process providing a vector with at least one element, which belongs to the test data set. The decision is based on a supervised learning classifier 301, which is trained with a training data set 302. The classifier 304 decides on the basis of a Bayesian linear or quadratic discriminant classifier, a support vector machine or the k-Nearest-Neighbour (kNN) classifier and with a supervised learning approach based on the training data set, whether the patient is awake or asleep. The more representative the training data are, the better the accuracy and performance of classification 304.
The sleep restriction therapy is a non-pharmacological method that can be used to treat insomnia either alone or in combination with pharmacological treatment. There is a natural tendency among poor sleepers to increase the amount of time spent in bed in an effort to provide more opportunity for sleep, a strategy that is more likely to result in fragmented and poor quality sleep.
The sleep restriction therapy consists of curtailing the amount of time spent in bed to the actual amount of time asleep. Time in bed is subsequently adjusted on the basis of the sleep efficiency calculation for a given period of time, which is usually the preceding week. For example, if a person reports sleeping an average of 6 hours per night out of 8 hours spent in bed, the initial prescribed sleep window would be 6 hours.
The subsequent allowable time in bed by about 15 to 20 minutes for a given week when sleep efficiency exceeds 90%, decreased by the same amount of time when the sleep efficiency is lower than 80% and kept stable when the sleep efficiency falls between 80 and 90%. Alternatively, a therapist may set the upper limit at 85% sleep efficiency. Adjustments are made periodically, for example weekly, until optimal sleep duration is achieved. Variations in implementing this procedure may involve changing the time in bed on the basis of a moving average of the sleep efficiency, for example, of the past three to five days, or changing it on a weekly basis regardless of change of the sleep efficiency. This procedure improves sleep continuity through a mild sleep deprivation and a reduction of sleep anticipatory anxiety. To prevent excessive daytime sleepiness, the time in bed should not be reduced to less than five hours per night.
As can be seen in FIG. 4, there is a preferred embodiment of the sleep restriction therapy method based on the sleep efficiency calculation which is provided by the present invention.
In the first step 401, the medical practitioner initializes wake-up-time and go-to-bed time. In step 402 data is collected for five days, which also includes information from questionnaires.
In step 403 the mean sleep efficiency of the last five days is calculated. In step 404 a case decision is made, deciding whether the mean sleep efficiency is lower than 80%, 80 to 90% or greater than 90%.
In case the mean sleep efficiency is lower than 80%, in step 406, the patient is asked to shorten the time in bed by 15 minutes, in case the mean sleep efficiency is greater than 90%, in step 405 the patient is asked to prolong time in bed by 15 minutes. The process recontinues at step 402 with the collection of sleep information for the next five days. In case the mean sleep efficiency is between 80 to 90%, in step 407 positive feedback is given to the patient and in step 408 the wake-up-time and the go-to-bed time will be kept for one more day and the process continues at step 403.
The disclosed concept may also be identified as a system 1000 structured to facilitate cognitive behavioral therapy for a patient having insomnia and may utilize the method set forth below. Prior to discussing the system 1000 and method, the following definitions are noted.
As used herein, "unobtrusive sensor" means a sensor that is either not attached directly to the patient's body, e.g. by an adhesive, or is wireless. A sensor that is not coupled to the patient's body includes, for example, a ferro-electret foil disposed immediately adjacent, or in contact with, the patient's thorax is an "unobtrusive sensor" because it is not attached to the patient and the patient may move freely. Other "unobtrusive sensors" include a piezoresistive strain gage coupled to the patient's bed, an ECG sensor embedded in fabric, e.g. a pillow, bedding, pajamas, nightcap or a radar/video system structured to detect movement. An "unobtrusive sensor" that may be coupled to the patient's body includes any small/light sensor that may be installed on a bracelet or similar construct. One example of such an unobtrusive sensor is an accelerometer. Any sensor that has a wire extending between the patient and a device is not an "unobtrusive sensor."
As used herein, "sleeping activity data" is data, and preferably biometric data, representing a measurable action, whether voluntary or involuntary, that is an indication of a patient's state of consciousness, i.e. asleep or awake.
As used herein, an "assessment" is one or more questions/statements answered on a scale, e.g. 1 to 5, days per week, very sleepy to wide awake, etc. Such answers are converted to "points," e.g. sleepy=1 point, wide awake=5 points. If more than one question is related, the points may be combined into a score. The act of combining points may be simple addition, but may also include a multiplier for synergistic combinations. For example, an assessment may include the following three questions/statements: Rate the severity of (1) difficulty falling asleep, 1=none, 5=severe, (2) difficulty staying asleep, 1=none, 5=severe, and (3) how many cups of coffee do you drink after 6:00 PM. Here, the "points" associated with questions (1) and (2) may be combined through simple addition, whereas the "points," e.g. number of cups of coffee consumed, may be a multiplier. Further, as is known, the final combined score for an assessment is typically compared to one or more predetermined threshold values associated with that assessment. For example, one assessment may determine the level or severity of the patient's insomnia and another assessment may determine the type of insomnia associated with the patient. Named assessments include, but are not limited to: Epworth Sleepiness Scale (ESS), Pre-Sleep Arousal Scale, Sleep Disturbance Questionnaire, Sleep Hygiene Practice Scale, Caffeine Knowledge Quiz, The Sleep Behavior Self-Rating Scale, The Glasgow Content of Thoughts Inventory, The Glasgow Sleep Effort Scale, Pittsburgh Sleep Quality Index (PSQI), and the Multidimensional Fatigue Inventory (MFI).
As used herein, a "threshold value" may also be a range of values. That is, a "threshold value" may exist as a maximum value, a minimum value, or a range of (un)acceptable values.
As shown in FIG. 5, the system 1000 structured to facilitate cognitive behavioral therapy for a patient having insomnia includes a communication assembly 1002, a sensor system 1004, a first processing unit 1006, second processing unit 1008, third processing unit 1010, a fourth processing unit 1012, and a display 1014. The communication assembly 1002 is structured to provide for electronic communication between the components identified above. That is, each component is coupled to, and in electrical communication with, the communication assembly 1002. The communication assembly 1002 and the components may be wirelessly coupled to the various components. The communication assembly 1002 is preferably coupled to, and in electrical communication with, an electronic communication network 1001, such as, but not limited to, the Internet.
The sensor system 1004 has at least one sensor 1020. The sensor system 1004 is structured to detect sleeping activity data and to provide a sensor system signal incorporating the sleeping activity data. As used herein, "sleeping activity data" includes at least, heart rate data, respiratory rate data and patient body movement data. As is known, data from such sensors 1020, e.g. motion data detected by an actigraph and heart rate data detected by an ECG, may be combined to determine, or estimate, if the patient is awake or asleep. It is again noted that the ECG sensor associated with the disclosed method is an unobtrusive sensor, i.e. a sensor incorporated into the patient's clothes rather than a traditional ECG having wires/leads coupled to a sensor adhered to the patient's skin. Other sensors that may be used include, but are not limited to inductance plethysmography sensors structured to detect respiratory effort and Emfit foil, structured to measure ballistocardiography (cardiac activity, respiratory activity and body motion activity).
The sensor system 1004, that is each at least one sensor 1020, is coupled to, and in electronic communication with, the communication assembly 1002. Each at least one sensor 1020 is an unobtrusive sensor structured to produce a sensor signal having at least one feature, as set forth above. The at least one sensor 1020 may be, but is not limited to, an ECG sensor 1022 and/or an actigraph sensor 1024. The ECG sensor 1022 is structured to detect heart rate data and respiratory rate data. The heart rate data and respiratory rate data being a feature incorporated into an ECG sensor 1022 signal. The actigraph sensor 1024 is structured to detect patient body movement data. The patient body movement data being a feature incorporated into an actigraph sensor 1024 signal. Specific types of the at least one sensor 1020 are discussed above. Thus, the at least one sensor 1020 may be a single accelerometer, a single actigraph, etc.
Each processing unit, i.e. the first, second, third and fourth processing units 1006, 1008, 1010, 1012, each include operational elements such as, but not limited to, a programmable logic circuit (PLC) and a communication system (neither shown), as is known in the art. The operational elements of each processing unit 1006, 1008, 1010, 1012 may also include a memory device (not shown) such as, but not limited to, random access memory (RAM), read-only memory (ROM), flash memory, and/or a hard drive, which may be a disk or solid state. The memory device is structured to store one or more sets of executable instructions, hereinafter routines 1030, as well as collected data, other data provided with the routines 1030, and data downloaded via the electronic communication network 1001. As is known, any data stored on each processing unit 1006, 1008, 1010, 1012 may be communicated to a remote location, such as, but not limited to a medical professional's office, via the communication assembly 1002 and electronic communication network 1001. Similarly, data and/or routines 1030 may be downloaded to each processing unit 1006, 1008, 1010, 1012 from a remote location, such as, but not limited to a medical professional's office, via the communication assembly 1002 and electronic communication network 1001. It is understood that the operational elements of each processing unit 1006, 1008, 1010, 1012 operate cooperatively to perform any function that the processing unit is structured to accomplish.
The operational elements of each processing unit 1006, 1008, 1010, 1012 are preferably disposed in a housing (shown schematically). The operational elements of two or more processing units 1006, 1008, 1010, 1012 may be disposed in a shared housing (not shown) and may operate cooperatively. That is, for example, routines 1030 for each processing unit 1006, 1008, 1010, 1012 may be stored on a single storage device. At least one processing unit 1006, 1008, 1010, 1012, however, has a PLC that is not a general purpose PLC. Preferably, the first processing unit's 1006 PLC is not a general purpose PLC.
The first processing unit 1006 corresponds to the pre-processing unit 102 described above. That is, the first processing unit 1006 is structured to perform the steps associated with the pre-processing unit 102 described above. The first processing unit 1006 is coupled to, and in electronic communication with, the communication assembly 1002. The first processing unit 1006 is structured to receive sensor system signal, via the communication assembly 1002, and to convert the sleeping activity data into sleep pattern data. Sleep pattern data includes a patient's time in bed, a patient's total sleep time, a patient's total wake time, a patient's sleep efficiency, a patient's sleep onset latency, a patient's awakenings after sleep onset and a patient's snooze time. As used herein, "time in bed" means the time the patient spends in bed, whether asleep or awake. As used herein, "total sleep time" is that portion of the time in bed that the patient is asleep. As used herein, "sleep onset latency" is the time between the patient getting into bed and first falling asleep. As used herein, "wake after sleep onset" is that portion of the time in bed that the patient is awake from the first time he/she fell asleep to the moment he/she finally woke up in the morning. As used herein, "snooze time" is the duration from the final awakening to the time when the patient gets out of bed. As used herein, "total wake time" is that portion of the time in bed that the patient is awake. As used herein, "sleep efficiency" is the ratio of total sleep time to time in bed. As used herein, "awakenings after sleep onset" is the number of times that the patient wakes after first falling asleep. Thus, the first processing unit 1006 is structured to process the heart rate data, respiratory rate data, and patient body movement data to determine at least one of the patient's time in bed, the patient's total sleep time, the patient's total wake time, the patient's sleep efficiency, the patient's sleep onset latency, the patient's awakenings after sleep onset and the patient's snooze time.
The second processing unit 1008 corresponds to the user interface device 107 described above. That is, the second processing unit 1008 is structured to perform the steps associated with the user interface device 107 described above. The second processing unit 1008, preferably, includes the display 1014. The second processing unit 1008 includes a routine 1030 that is structured to present the user interface 108 on the display 1014. The second processing unit 1008, preferably, includes an input assembly 1034 having one or more input devices such as, but not limited to, a keyboard 1036 and a mouse 1038 (or trackball, or touch screen, or any other device that provides a mouse-like functionality). As is known, the patient may enter patient input data via the input assembly 1034 and the user interface 108. The user interface 108 is structured to present one or more assessments, discussed below, and to allow the user to input data into a sleep log. The second processing unit 1008 is coupled to, and in electronic communication with, the communication assembly 1002.
The third processing unit 1010 corresponds to the feature extraction unit 103, the sleep/wake classifier unit 104 and the sleep efficiency calculation unit 106 described above. That is, the third processing unit 1010 is structured to perform the steps associated with the feature extraction unit 103, the sleep/wake classifier unit 104 and the sleep efficiency calculation unit 106 described above. The third processing unit 1010 is coupled to, and in electronic communication with, the communication assembly 1002. The third processing unit 1010 is structured to receive the sleep pattern data and the patient input data, via the communication assembly 1002, and to perform an analysis thereon. The analysis creates a patient sleep profile. As used herein, a "patient sleep profile" is a collection of data including objective data, such as the patient body movement data, and subjective data, such as the patient input data. The patient sleep profile includes the patient's sleep history, sleep patterns, qualitative and quantitative measures of insomnia, identification of insomnia-related factors and identification of daytime consequences of insomnia Daytime consequences of insomnia include, but are not limited to, poor daytime energy, poor cognitive functioning (e.g. concentration, focus, attention, and memory) and poor mood and motivation.
A qualitative measure of insomnia includes, but is not limited to, a subjective estimation of sleep quality e.g. poor to excellent or as on a Likert scale of 1 to 10. A quantitative measure of insomnia includes, but is not limited to, the Insomnia Severity Index (ISI) score, discussed in detail below, subjective and objective estimates of Sleep Onset Latency, Wake After Sleep Onset, and Snooze time. Such quantitative measures of insomnia may be compared to a standard threshold, e.g. the Sleep Onset Latency threshold is set to 30 minutes. Such a comparison may be used to assess the type of sleep disturbance (problem with sleep initiation, sleep maintenance or early awakenings, etc.). Further, subjective and objective estimates of Sleep Efficiency, may be compared to standard thresholds (e.g. 90%) to assess whether or not the patient sleeps in a consolidated way.
The third processing unit 1010 is structured to send the sleep pattern data, the patient input data and the patient sleep profile to a medical professional via the electronic communications network 1001.
The fourth processing unit 1012 corresponds to the sleep restriction algorithm device 106 described above. That is, the fourth processing unit 1012 is structured to perform the steps associated with the sleep restriction algorithm device 106 described above. The fourth processing unit 1012 is coupled to, and in electronic communication with, the communication assembly 1002. The fourth processing unit is structured to analyze the patient sleep profile. The analysis performed by the fourth processing unit 1012 is discussed below. The fourth processing unit 1012 is further structured to provide a course of therapy related to the patient sleep profile. That is, the fourth processing unit 1012 includes a memory device 1050 having stored thereon a plurality of interactive therapeutic instruction modules 1060, 1062, 1064, 1066, 1068, 1070.
The therapeutic instruction modules 1060, 1062, 1064, 1066, 1068, 1070 are, generally, associated with insomnia related factors. Insomnia related factors include, but are not limited to behavioral and cognitive factors. For example, behavioral factors include, but are not limited to, sleep restriction, scheduling of bedtime and wake time. Behavioral factors may note a change in the patient's pattern, irregular sleep/wake times and/or when a patient enters in a compensatory strategy, e.g. going to bed earlier and/or sleeping in and/or napping so as to compensate for a bad night's sleep. Behavioral factors also include stimulus control, i.e. association of bed-sleep or when the conditioning between bed and sleep ceases. That is, bed and bedtime become cues for activity rather than cues for sleep. For example, the patient may lie awake in bed, which confuses the connection in the brain between bed and sleep and maintains wakefulness. Other behavioral factors include the patient's pre-sleep routine and other habits that may impact sleep, e.g., alcohol, caffeine, nicotine, dinner habits, bedroom environment, etc. Cognitive factors include, but are not limited to, the patient's mind racing during the night, i.e. hyperarousal at bedtime, and/or worries, anxiety about sleep, e.g., amplification about consequences of insomnia, perceived control and predictability of sleep, unrealistic expectations about sleep.
Each interactive therapeutic instruction module 1060, 1062, 1064, 1066, 1068, 1070 contains three parts: (1) therapeutic advice, given on the first day a patient accesses a module, (2) the general information delivered in daily tips on the remaining days of the module, and (3) a goal setting section, accessible on the first day a patient accesses the module. The therapeutic advice consists of recommendations/interventions which are based on the standard CBT-I. The therapeutic advice and daily tips targets those factors that maintain the problems or make them worse by a particular patient and may be related to the goals identified by the patient. That is, while the general, or global, goal is to improve sleep, the patient may know of a specific problem and identify a specific goal related to that problem. For example, the patient may wish to learn to fall asleep more easily and/or learn to fall back to sleep more easily after waking in the middle of the sleep cycle. Alternatively, the patient may wish to learn how to feel more refreshed in the morning, improve their daytime energy, improve their daytime performance, improve their cognitive functioning (concentration, focus, attention and memory) and/or improve their mood and motivation. Or, as another alternative, the patient may wish to identify their actual sleep pattern or determine how many hours of sleep they have during the night. The coaching and/or other advice may be related to the global goal or one or more of the more specific goals.
The fourth processing unit 1012 is further structured to organize the selection and order of the therapeutic instruction modules based upon the patient's sleep profile. Finally, the fourth processing unit 1012 is structured to present the organized therapeutic instruction modules on the display 1014.
The display 1014 is coupled to, and in electronic communication with, the communication assembly 1002. The display 1014 is structured to present the user interface 108, discussed above, as well as any other information.
Prior to discussing the method associated with the system 1000, the following assessments are noted. The assessments, or individual portions thereof, are used during the various analyses discussed below. One known assessment is the Insomnia Severity Index (ISI) which includes the following questions/statements:
Insomnia Severity Index
1. Please rate theNoneMildModerateSevereVery Severecurrent severity of01234your insomnia.a. Difficulty falling□□□□□asleepb. Difficulty□□□□□staying asleepc. Problem waking□□□□□up too early2. HowVery SatisfiedA LittleSomewhatMuchVery Dissatisfiedsatisfied/dissatisfied01234are you with your□□□□□current sleep pattern3. To what extent doNot At AllA LittleSomewhatMuchVery Muchyou consider yourInterfering123Interferingsleep problem to0□□□4interfere with your□□daily functioning(e.g., daytimefatigue, ability tofunction at work,daily chores,concentration,memory, mood,etc.)?4. How noticeableNot At AllBarelySomewhatMuchVery Muchto others do youNoticeable1Noticeable3Noticeablethink your sleeping0□2□4problem is in terms□□□of impairing thequality of your life?5. HowNot At AllA LittleSomewhatMuchVery Muchworried/distressedWorried1Worried3Worriedare you about your0□2□4current problem?□□□
Another known assessment, the Insomnia Frequency Questionnaire (IFQ), includes responses indicating the number of days per week the patient suffers from the indicated symptoms and includes the following questions/statements:
Insomnia symptomsNumber of nights per week1.I took medication at bedtime to sleep012345672.I took medication in the middle of the night to go back to sleep012345673.It took more than 30 minutes to fall asleep at the beginning of the night012345674.I was awake for more than 30 minutes in the middle of the night012345675.I woke up at least 30 minutes before my planned (or desired) rise time01234567and was unabe to resume sleep6.My sleep was fragmented (woke up 3 or more times)012345677.I felt unrefreshed or tired when I got up to start my day012345678.I struggled for >30 minutes to get myself out of bed to start the day012345679.I got less than 6.5 hours of sleep0123466710.I got sufficient amount of sleep01234567Daytime symptoms related to poor sleepNumber of days per week11.During the day, I was concerned that I might not sleep well that night0123456712.During the day I was concerned that my poor sleep might affect my01234567performance, well-being, or helath13.Poor sleep had limited my ability to function and to cope during the day0123456714.Poor sleep lead to low energy or feelings of tiredness or fatigue most of01234567the day15.Poor sleep lead to trouble concnetrating or focusiing0123456716.Poor sleep lead to feelings of depressed mood0123456717.Poor sleep lead to irritability, tension, or or being on edge0123456718.Poor sleep lead to having to push myself to get things done0123456719.Poor sleep lead aches and pains (e.g. headaches, muscle achels,01234567stomach ache)
The sleep log includes both questions/statements rated on a scale, and questions/statements to which the response is a time period measured in hours and minutes. The sleep log includes the following questions/statements:
Sleep Log
Default sleep log (during baseline assessment and program):                1. Please rate the quality of your sleep last night:                    Using a scale of 1-5, very poor . . . excellent                        2. How did you feel when you woke up this morning?                    Using a scale 1-5, exhausted . . . refreshed                        3. What time did you turn off the light to go to sleep last night?                    →Enter time as hh:mm                        4. How long did it take you to fall asleep?→Enter in minutes        5. What was the time when you finally woke up this morning?→Enter time as hh:mm        6. Please estimate the total amount of time (in minutes) you spent awake during the night (from the first time you fell asleep to the moment you finally woke up this morning)→Enter in minutes        7. What time did you get out of bed this morning?→Enter time as hh:mm        8. How tired did you feel yesterday?                    Using a scale of 1-5, very tired . . . very alert                        9. Were there yesterday unusual external factors that may have disrupted your sleep?(e.g. health, work, family, environment, etc.)                    Using a scale of 1-5: many disruptive factors . . . no disruptive factors
Another sleep assessment is the DBAS-16 which relates to a patient's beliefs about sleep and includes statements which the patient rates such as, but not limited to:
Several statements reflecting people's beliefs and attitudesabout sleep are listed below. Pleaseindicate to what extent you personally agree or disagree with each statement. There is no right orwrong answer. For each statement, circle the number thatcorresponds to your own personalbelief. Please respond to all items even though some may not applydirectly to your own situation.  1. I need 8 hours of sleep to feel refreshed and function well during the day.   2. When I don't get proper amount of sleep on a given night, I need to catchup on the next day by napping or on the next night by sleeping longer.   3. I am concerned that chronic insomnia may have seriousconsequences on my physical health.   4. I am worried that I may lose control over my abilities to sleep.   5. After a poor night's sleep, I know that it will interfere withthe daily activities on the next day.   6. In order to be alert and function well during the day, I believeI would be better off taking a sleeping pill rather than havinga poor night's sleep.   7. When feel irritable, depressed, or anxious during the day,it is mostly because I did not sleep well the night before.   8. When I sleep poorly on one night, I know it will disturbmy sleep schedule for the whole week.   9. Without an adequate night's sleep, I can hardly function the next day.  10. I can't ever predict whether I'll have a good or poor night's sleep.  11. I have little ability to manage the negative consequences of disturbedsleep.  12. When I feel tired, have no energy, or just seem not to function well duringthe day, it is generally because I did not sleep well the night before.  13. I believe insomnia is essentially the result of a chemical imbalance.  14. I feel insomnia is ruining my ability to enjoy life andprevents me from doing what I want.  15. Medication is probably the only solution to sleeplessness.  16. I avoid or cancel obligations (social, family) after a poor night's sleep.
Utilizing the system 1000 structured to facilitate cognitive behavioral therapy for a patient having insomnia the patient may receive cognitive behavioral therapy for insomnia according to a method having the following steps. Initially, the method includes the step of monitoring 2000 a patient's sleep utilizing a sensor system 1004 having at least one unobtrusive sensor 1020. As noted above, the sensor system 1004 is structured to detect sleeping activity data. The method further includes the steps of gathering 2002 patient input data, combining 2006 the patient input data and the sleeping activity data to create a patient sleep profile, analyzing 2008 a patient sleep profile to determine a course of therapy, and presenting 2010 the course of therapy to the patient on a display 1014.
The step of monitoring 2000 a patient's sleep utilizing a sensor system having at least one unobtrusive sensor includes the step of utilizing 2020 an ECG sensor and an actigraph sensor. As noted above, the ECG sensor 1022 is structured to detect heart rate data and respiratory rate data and the actigraph sensor 1024 is structured to detect patient body movement data. The step of monitoring 2000 a patient's sleep utilizing a sensor system having at least one unobtrusive sensor further includes the step of processing 2022 the heart rate data, respiratory rate data, and patient body movement data to determine at least one of: the patient's time in bed, the patient's total sleep time, the patient's total wake time, the patient's sleep efficiency, the patient's sleep onset latency, the patient's awakenings after sleep onset and the patient's snooze time.
The step of gathering 2002 patient input data includes the steps of: gathering 2030 data on the patient's sleep history, gathering 2032 data on the patient's medical history, having 2034 the patient complete an insomnia severity index assessment, and having 2036 the patient maintain a sleep log. As noted above, the patient input data is input via the second processing unit 2008 and, more specifically, via the input assembly 1034 and the user interface 108.
The step of analyzing 2008 a patient sleep profile to determine a course of therapy may include the steps of: analyzing 2038 the patient input data and the sleeping activity data to identify the factors contributing to the patients insomnia, identifying 2039 the type of insomnia associated with the patient, analyzing 2040 the patient input data and the sleeping activity data to determine the severity of the patient's insomnia, analyzing 2042 the patient input data to determine the severity of the effect of the patient's insomnia on daytime activities, analyzing 2044 the patient input data to determine the type of insomnia suffered by the patient, and, based on the severity of the patient's insomnia, the severity of the effect of the patient's insomnia on daytime activities, and the type of insomnia suffered by the patient, recommending 2046 to the patient one of the following: referral to a medical professional for treatment, recommending an interactive course of therapy to the patient, recommending no treatment.
If the patient is recommended an interactive course of therapy, the therapy is, preferably, provided to the patient via the display 1014 which presents the user interface 108. That is, the steps of analyzing 2008 the patient sleep profile to determine a course of therapy and presenting 2010 the course of therapy to the patient on a display 1014 include the steps of: providing 2050 a plurality of interactive therapeutic instruction modules, each module related to an aspect of insomnia, based on the patient sleep profile, determining 2052 which of the interactive therapeutic instruction modules are relevant to the patient's insomnia, determining 2054 the sequence of the interactive therapeutic instruction modules to be presented to the patient, presenting, 2056 in the predetermined sequence, the interactive therapeutic instruction modules to the patient on a display 1014.
As noted above, each interactive therapeutic instruction module 1060, 1062, 1064, 1066, 1068, 1070 contains three parts: (1) therapeutic advice, given on the first day a patient accesses a module, (2) the general information delivered in daily tips on the remaining days of the module, and (3) a goal setting section, accessible on the first day a patient accesses the module. The plurality of interactive therapeutic instruction modules include a sleep scheduling module 1060, an association bed-sleep module 1062, a cognitive restructuring module 1064, a coping strategy module 1066, a relaxation module 1068, and a lifestyle module 1070. In addition to the general information associated with each interactive therapeutic instruction module 1060, 1062, 1064, 1066, 1068, 1070, as discussed above, the specific modules each have additional processes as discussed below. It is further noted that the interactive therapeutic instruction modules 1060, 1062, 1064, 1066, 1068, 1070 may be (1) stored on the fourth processing unit 1012, i.e. in the storage device for the fourth processing unit 1012, (2) downloaded to the fourth processing unit 1012 via the electronic communication network 1001 and the communication assembly 1002, or (3) a combination thereof. That is, the fourth processing unit 1012 may store a portion of each interactive therapeutic instruction module 1060, 1062, 1064, 1066, 1068, 1070 and have updates or additional information downloaded as well.
For example, if the sleep scheduling module 1060 is recommended to the patient, the method of providing cognitive behavioral therapy for insomnia includes the further steps of: providing 2060 a sleep scheduling assessment structured to determine a first and a second sleep scheduling score, determining 2062 the patient's first and second sleep scheduling scores, and determining 2064 whether patient has an irregular sleep schedule. The determination 2062 of the patient's first and second sleep scheduling scores may be based on both objective and subjective data. For example, objective data, such as, but not limited to, sleep efficiency data collected by the sensors, as discussed above. Subjective data may be based on an assessment such as the following questions/statements:                I set myself a regular rising time each morning regardless of my sleep quality and duration (rated from strongly disagree to strongly agree);        When I don't get proper amount of sleep, on a given night, I need to catch up on the next day by napping or on the next night by sleeping longer (rated from strongly disagree to strongly agree); and        Rating further translated into a score.
These ratings are converted into the patient's first and second sleep scheduling scores, e.g. by assigning a score to the answer and combining the score with objective data. Based on the factors that are determined, e.g. if patient's first sleep scheduling score is greater than a first sleep scheduling threshold value, the method further includes providing 2066 sleep schedule coaching, and/or, if the patient has an irregular sleep schedule and if patient's second sleep scheduling score is greater than a second sleep scheduling threshold value, providing 2068 sleep schedule coaching. In this module 1060, as well as all other modules 1062, 1064, 1066, 1068, 1070, the step of coaching is accomplished by providing information to the patient. Preferably, the information is provided via the display 1014 and in the form of text messages, e-mail, instant messages, etc. The coaching messages are, preferably, formatted as reminders, suggested therapeutic actions, progress reports, etc., and are typically related to a goal, as discussed above.
If the association bed-sleep module is recommended to the patient, the method of providing cognitive behavioral therapy for insomnia includes the further steps of: providing 2070 an association bed-sleep assessment structured to determine an association bed-sleep score, determining 2072 the patient's association bed-sleep score, and, if patient's association bed-sleep score is greater than an association bed-sleep threshold value, providing 2074 association bed-sleep coaching. The association bed-sleep assessment may include such questions/statements as:                Please indicate how often you do the following things in your bed before falling asleep or while in your bedroom. Complete the form by considering what you would do in an average week:        Watch TV, work, use the PC, listen to the radio or music, have a conversation with someone, speak on the telephone, eat and drink, read a book or magazine: never/rarely/sometimes/often/quite often;        I switch the light off as soon as I get into bed:        never/rarely/sometimes/often/quite often; and        I spend a lot of time lying awake in bed at night: never/rarely/sometimes/often/quite oftenwhich are converted into the patient's association bed-sleep score.
If the cognitive restructuring module 1064 is recommended to the patient, the method of providing cognitive behavioral therapy for insomnia includes the further steps of: providing 2080 a first sleep concept assessment structured to determine a first sleep concept score and determining 2082 the patient's first sleep concept score. If the patient's first sleep concept score is greater than a first sleep concept threshold value, performing 2084 a second sleep concept assessment to determine an amplification score, a perceived control score, and an unrealistic expectation score. Targeted coaching is provided related to any aspect of the sleep concept assessment wherein the patient is over the associated threshold value. That is, if patient's amplification score is greater than an amplification threshold value, providing 2086 amplification coaching. If the patient's perceived control score is greater than a perceived control threshold value, providing 2088 perceived control coaching. Further, if a patient's unrealistic expectation score is greater than an unrealistic expectation threshold value, providing 2089 unrealistic expectation coaching. The sleep concept assessment may include such questions/statements as:                Qa: "I put too much effort into sleeping at night when it should come naturally;"        Qb: "I worry about not sleeping if I am in bed at night and cannot sleep;" and        Qc: "I get anxious about sleeping before I go to bed at night"as well as questions from the ISI, the IFQ and the DBAS-16 which are converted into a the patient's first and second sleep concept scores.
If the coping strategy module is recommended to the patient, the method of providing cognitive behavioral therapy for insomnia includes the further steps of: providing 2090 a first coping strategy assessment structured to determine a coping strategy score, determining 2092 a coping strategy score and, if patient's coping strategy score is greater than a coping strategy threshold value, providing 2094 coping strategy coaching. The coping strategy assessment may include such questions/statements as:                I have little ability to manage the negative consequences of disturbed sleep: never true, seldom true, sometimes true, often true, very often true;        I feel my sleep problems are ruining my ability to enjoy life and prevent me from doing what I want (social/occupational/family obligations); and        I avoid or cancel obligations (social, family) after a poor night's sleep which are converted into the patient's coping strategy score.
If the relaxation module is recommended to the patient, the method of providing cognitive behavioral therapy for insomnia includes the further steps of: providing 2100 a relaxation assessment structured to determine a transition time score, a worry time score, and a behavioral score, determining 2102 a transition time score, a worry time score, and a behavioral score. If the patient's transition time score is greater than a transition time threshold value, the method includes the step of providing 2104 transition time coaching. If the patient's worry time score is greater than a worry time threshold value, the method includes the step of providing 2106 worry time coaching. If the patient's behavioral score is greater than a behavioral threshold value, the method includes the step of providing 2108 behavioral coaching. The relaxation assessment may include such questions/statements as:                Q1: What do you usually do before going to bed? Watching TV, reading, working, using the PC, doing housework, other (checkboxes);        Q2: Would you consider your pre-bed activities as a routine? Yes/no;        Q3: How often do you experience one of the following problems while trying to fall asleep:                    My mind keeps turning things over;            My thinking takes a long time to unwind; and            I am unable to empty my mind                        (Never, seldom, sometimes, often, very often);        Q4: How often do you find it hard to physically "let go" and relax your body? Never, seldom, sometimes, often, very often;        Q5: How relaxed are you before going to bed? and        Q6: Do you think that stress/high workload makes your sleep poorer?which are converted into the patient's relaxation score.
If the lifestyle module is recommended to the patient, the method of providing cognitive behavioral therapy for insomnia includes the further steps of: providing 2110 a lifestyle assessment structured to determine a physical activity score, an alcohol score, a caffeine score, a napping score, a nicotine score, an intake score (i.e. food and/or calorie intake score), and an environment score and determining 2111 a physical activity score, an alcohol score, a caffeine score, a napping score, a nicotine score, an intake score, and an environment score. If the patient's physical activity score is greater than a physical activity threshold value, the method includes the step of providing 2112 physical activity coaching. If the patient's alcohol score is greater than an alcohol threshold value, the method includes the step of providing 2114 alcohol coaching. If the patient's caffeine score is greater than a caffeine threshold value, the method includes the step of providing 2116 caffeine coaching. If the patient's napping score is greater than a napping threshold value, the method includes the step of providing 2118 napping coaching. If the patient's nicotine score is greater than a nicotine threshold value, the method includes the step of providing 2120 nicotine coaching. If the patient's intake score is greater than or less than the intake threshold value (or range), the method includes the step of providing 2122 intake coaching. That is, if the patient is eating too much or too little, the patient will be beyond the intake threshold range. If the patient's environment score is greater than an environment threshold value, the method includes the step of providing 2124 environment coaching. The lifestyle assessment may include, but is not limited to, such questions/statements as:                Q1: How often do you exercise and how long? X times per week, y minutes;        Q2: Do you exercise in the 4 hours preceding your bedtime?;        Q3: How many glasses of alcohol do you drink in the evening?        X glasses beer-wine-stronger (checkboxes);        Q4: How many cups of caffeinated drinks do you drink per day (coffee, tea, cola)? X cups, y of which in the evening;        Q5: Do you take naps? x times per week, y minutes;        Q6: When do you usually nap? Before/after 4 pm (checkboxes);        Q7: Do you smoke? x cigarettes per day;        Q8: Do you smoke before going to bed? In the middle of the night? Yes/no;        Q9: When you wake up during the night, you often:                    Eat something?;            Have something to drink?; and            Got to the bathroom?                        Q10: Do you have regular meal times?;        Q11: Do you feel sometimes too hot or too cold in your bed?;        Q12: Do you like your bedroom?;        Q13: Is your sleep sometimes disturbed by noise?; and        Q14: Is your sleep sometimes disturbed by light?which are converted into the patient's physical activity score, an alcohol score, a caffeine score, a napping score, a nicotine score, an intake score, and an environment score.
As noted above, and as shown in FIG. 6, the method is, preferably, an iterative process wherein the patient's sleep profile is updated 2130 so as to reflect the patient's current level of insomnia. While the patient input data and the sleeping activity data may be updated every day, preferably the interactive course of therapy is updated regularly, but less frequently than daily. Preferably, the interactive course of therapy is updated weekly. Once the patient's patient sleep profile has been updated, the step of analyzing 2008 a patient sleep profile to determine a course of therapy is repeated. That is, the following steps, and any sub steps may be repeated: the steps of: analyzing 2040 the patient input data and the sleeping activity data to determine the severity of the patient's insomnia; analyzing 2042 the patient input data to determine the severity of the effect of the patient's insomnia on daytime activities; analyzing 2044 the patient input data to determine the type of insomnia suffered by the patient; and, based on the severity of the patient's insomnia, the severity of the effect of the patient's insomnia on daytime activities, and the type of insomnia suffered by the patient, recommending 2046 to the patient one of the following: referral to a medical professional for treatment; recommending an interactive course of therapy to the patient; and recommending no treatment.
Further, as the third processing unit 1010 is structured to send the sleep pattern data, the patient input data and the patient sleep profile to a medical professional via the electronic communications network 1001, the step of analyzing 2008 a patient sleep profile to determine a course of therapy may also include the steps of: having 2050 a medical professional review the sleep pattern data, the patient input data and the patient sleep profile, and having 2052 the medical professional provide direct feedback to the patient via the electronic communications network 1001. These steps may be repeated after an update of the sleep pattern data, the patient input data and the patient sleep profile.
One of ordinary skill in the art will appreciate many variations and modifications within the scope of this invention. This method and system will be used mainly for preferably insomnia patients at home or for patients at home or in hotels, for mobile patients in the hospital environment, during transport or at home but there are also applications possible for hospitalized patients. Also, devices could make use of this invention that are intended for healthy persons or even animals. Further, while the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments.
Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.
While specific embodiments of the invention have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of invention which is to be given the full breadth of the claims appended and any and all equivalents thereof.

Method and system for providing behavioural therapy for insomnia


An improved apparatus for plasma treatment of silicon semiconductor wafers is proposed in which the end point of the plasma etching or ashing can be readily detected by monitoring with an optical fiberscope penetrating the wall of the plasma reaction chamber with one terminal located inside the plasma reaction chamber with a condenser lens attached thereto and the other terminal located outside the plasma reaction chamber at a distanced position free from the influence of the high frequency electric field inherent to plasma generation which otherwise interferes with the photoelectric recording by the photocell connected to the outer terminal of the optical fiberscope.

The present invention relates to an apparatus for the precision treatment of a material such as high-purity silicon semiconductor wafers in which a monitoring means for detecting the end point of the plasma reaction is provided.
In the recent development of the semiconductor technology, a dry method of plasma etching is obtaining more and more wide applications in place of the conventional wet etching method with an etching solution giving a possibility of very fine working in the manufacture of ICs, LSIs, ultra-LSIs and the like. In spite of the very apparent advantages of the plasma etching method over the wet method, it is only recently that the method has come into current because of the difficulty in detecting the end point of the etching by the plasma reaction as a major problem.
Attempts have been made for monitoring the progress of the plasma reaction by spectrophotometrically recording the light emitted from the plasma through a window in the plasma reaction chamber with an expensive equipment composed of a grating spectrometer and a photomultiplier recording system (see W. R. Harshbarger et al., Kodak Microelectronics Seminar, October, 1976).


A humidifier is provided with a housing and a stacked unit inserted into the housing that is provided with water vapor-permeable membranes and frame parts, wherein the frame parts are stacked on each other and the water vapor-permeable membranes are arranged between the frame parts, respectively, so that the water vapor-permeable membranes are stacked on top of each other and are spaced apart from each other. The housing has supports that hold the stacked unit. The supports and the frame parts are connected to each other by a connection that is made of laterally projecting connecting tabs and a receiving groove extending in vertical direction, wherein the connecting tabs engage the receiving groove.

The invention concerns a humidifier, in particular for a fuel cell, comprising at least one stacked unit insertable into a housing and comprising several water vapor-permeable membranes which are arranged between frame parts so as to be stacked on top of each other and spaced apart from each other.
EP 1 261 992 B2 discloses a humidifier for a fuel cell that comprises a membrane stack with several membranes arranged on top of each other and spaced apart from each other and between which flow passages are formed. The water vapor-permeable membranes enable transfer of water molecules from humid air, which is passing through a flow passage adjoining a membrane, to a dry air stream in a flow passage extending on the opposite side of the membrane so that the dry air stream is enriched with moisture. The moisturized air stream is supplied to a fuel cell in which electric current is generated in an electrochemical reaction.
One membrane each is framed by two circumferentially extending frame parts stacked on each other, between which the membrane is fastened. The frame parts can be stacked on each other, the stacked unit is inserted into a housing into which the air streams with higher and lower moisture contents are fed.
It is the object of the invention to provide an efficiently operating humidifier of a constructively simply configuration that can be employed, for example, for a fuel cell.

Humidifier, in particular for a fuel cell


A method and apparatus for producing a composite part are provided to enable composite parts to be assembled with precise control over the orientation and spatial distribution of reinforcing or other particles within a matrix material. The method and apparatus use magnetic fields applied during various additive manufacturing processes to achieve complex particles orientations within each layer of the part. The composite parts can achieve enhanced properties, including mechanical, thermal, electrical and optical properties.

Additive manufacturing (sometimes termed 3D printing) is used to fabricate complex three-dimensional structures out of a variety of materials, such as polymers, metals, and ceramics, at levels of resolution down to tens of microns. Load-bearing mechanical structures from gears to functional fuel nozzles for jet engines have been produced by these techniques.
The printing of polymers has been accomplished mainly by extrusion-based direct-write methods for thermoplastics and stereolithography (SLA) based photo-polymerization for both thermoplastics and thermosets. Printed polymers are lightweight but relatively weak. Thus, 3D printing is now moving toward manufacturing fiber-reinforced polymer composites. Industrial automated fiber placement (AFP) printers have been developed that can print continuous strand carbon fiber, Kevlar, and fiberglass reinforced polymer. These robotic placement printers are limited to reinforcement fibers with length scales greater than millimeters and geometries larger than centimeters with orientation control limited to the X-Y plane.

Additive manufacturing of discontinuous fiber composites using magnetic fields


Activities involving a secure element (SE) in a mobile device include a background operation. When the SE initiates the background operation, it informs the mobile device of an estimated duration. The mobile device thus recognizes that the SE is not in a stuck state, and maintains a clock signal and a power flow to the SE. Firmware updates to the SE include erasing a non-volatile (NV) memory in the SE in parallel with firmware or software updates to other processor systems in the mobile device. Needed data, for example calibration data or cryptographic key data, is preserved by storing data from some processor systems in one or more supplementary security domains (SSDs) in the SE. When a given processor system completes a firmware update, the needed data is restored to the processor system from the SSD.

Most mobile devices are configured to utilize secure elements known as Universal Integrated Circuit Cards (UICCs) that enable the mobile devices to access services provided by Mobile Network Operators (MNOs). In particular, each UICC includes at least a microprocessor and a read-only memory (ROM), where the ROM is configured to store an MNO profile that the mobile device can utilize to register and interact with an MNO. Typically, a UICC takes the form of a small removable card (commonly referred to as a SIM card) that is configured to be inserted into a UICC-receiving bay included in a mobile device. In more recent implementations, however, UICCs are being embedded directly into system boards of mobile devices. An embedded or electronic UICC (eUICC) manages a number of electronic Subscriber Identity Modules (eSIMs) stored therein, thereby enabling a mobile device to communicate with a number of respective MNOs.
Typically, the UICC/eUICC processes various commands provided by the mobile device (e.g., commands issued by a baseband component included in the mobile device). In other words, the mobile device communicates the commands to the UICC/eUICC, and, in turn, the UICC/eUICC promptly processes the command and performs the associated operations. In some cases, when the UICC/eUICC needs more time to perform a particular operation, the UICC/eUICC communicates a "MORE TIME" command (e.g., as defined in ETSI TS 102 223), which indicates that additional time is needed by the UICC/eUICC to fully process the particular operation. According to some configurations, when a threshold number of "MORE TIME" commands are received by the mobile device, the mobile device assumes that the UICC/eUICC is stuck in an error state (e.g., the UICC/eUICC has ceased performing an operation). In response, the mobile device initiates an error recovery process for the UICC/eUICC where UICC/eUICC is reset and powered-down.


A fabric air duct with main discharge openings includes additional, much smaller openings that help ventilate the surface of the duct. Ventilating the surface of the duct with a slight yet even amount of airflow helps inhibit the accumulation of condensate and dust on the surface of the duct. In some embodiments, the duct includes a pliable sheet consisting of a rather porous fabric base material. To achieve an appropriately low level of airflow, a plastic coating on the fabric reduces, but does not eliminate the fabric's porosity. A calendering process then reduces the porosity even further. In some embodiments, the calendering process occurs before the plastic coating process. In other embodiments, the pliable sheet is substantially air impermeable, except for its main discharge openings. The sheet is then perforated with numerous smaller openings to achieve the desired amount of surface ventilation. In yet other embodiments, an emorizing or sueding process is used to abrade or nap the surface of a porous or non-porous base material to create a pliable sheet having a desired amount of porosity. From any of these processes, air ducts of various shapes may be formed, including air ducts that are circular, ½ round, and ¼ round in shape, as well as air ducts having a non-uniform cross-sectional shape across their lengths.

1. Field of the Invention
The subject invention generally pertains to pliable air ducts and more specifically to the air permeability of such a duct.
2. Description of Related Art
In HVAC systems (heating, ventilating, air conditioning), conditioned supply air discharged from a blower is often conveyed to various rooms or areas within a building by way of ductwork. Conventional ductwork is typically formed of sheet metal and is often installed above ceilings for convenience and aesthetics. But in warehouses, manufacturing plants and many other buildings, the ducts are suspended from the roof of the building and are thus exposed. This not only creates a poor appearance in many cases, but can create other problems as well.
For example, temperature differentials between an air duct and the air on either side of the duct wall can create condensation on both the interior and exterior of the duct. The presence of condensed moisture on the interior of the duct may form mold or bacteria that the duct then passes onto the room or other areas being supplied with the conditioned air. If an exposed sheet metal duct conveys relatively cool air, condensation can form on the exterior of the duct. The condensate may then drip onto the floor, inventory, and personnel below. The consequences of the dripping can range anywhere from a minor irritation to a dangerously slippery floor for the personnel, or complete destruction of the products it may drip on (especially in food-processing facilities).
Further, metal ducts with localized discharge registers have been known to create uncomfortable drafts and unbalanced localized heating or cooling within the building. In many food-processing facilities where the target temperature is 42 degrees Fahrenheit, a cold draft can be especially uncomfortable and perhaps unhealthy.
Many of the above problems associated with exposed metal ducts are overcome by the use of fabric ducts, such as DUCTSOX fabric ducts by Frommelt Safety Products Corporation of Milwaukee, Wis. Such ducts typically have a fabric wall that is air-permeable to broadly and evenly disperse the air into the room being conditioned or ventilated. If greater airflow in needed in certain areas, the fabric duct can be provided with additional discharge openings, such as air registers or cutouts in the fabric.
The porosity of conventional fabric can pass a substantial amount of air, which can be desirable in many applications where the airflow through the pores of the fabric is used primarily for evenly dispersing air into a room. However, some applications require airflow that is more directed toward certain areas of a room. In such cases, it may be desirable to have relatively large discharge openings provide most of the air airflow, while the pores of the fabric provide only enough airflow to inhibit dust and condensation from accumulating on the outer surface of the fabric material.
Unfortunately, it can be difficult to acquire an air duct material whose porosity provides an appropriately small amount of airflow, such as 2 cfm (two cubic feet per minute of air across one square-foot of material subject to a 0.02 psi air pressure differential). Standard fabric materials have been found to pass 40 cfm or more. Such materials have been calendered in an attempt to reduce the materials porosity. Although calendering conventional fabric does reduce its porosity temporarily, much of the effect is lost after the material is washed. Thus, simply calendering just any porous fabric is not a permanent solution to the problem.

Pliable air duct with dust and condensation repellency


A labeling machine has a revolving carrier on which at least one pickup element has a convexly curved receiving surface eccentrically journaled between the receiving surface and the center of curvature thereof. A drive is provided for each pickup element comprising a stationary cam and an arm which follows the cam and imparts to the pickup element an oscillating movement through a drive shaft. The stationary cam comprises a cylindrical cam truck and the drive shaft of each pickup element is coupled with the associated arm for movement by the stationary cam along the drive shaft through a cam consisting of two opposite parallel cam sections. The cam sections extend at an angle to a plane perpendicular to the shaft axis and cam followers are associated with these cam sections. The distance between the two cam sections is adjustable such that any free play present between them and the cam followers can be eliminated.

The invention relates to a labeling machine, especially for bottles, having a revolving carrier excentrically on which at least one pickup member having a convexly curved receiving surface is journaled, especially between the receiving surface and its center of curvature, and having a drive for each pickup element, which drive consists of a stationary cam and an arm which follows the cam and imparts an oscillating movement to the pickup element.
Such labeling machines are known (U.S. Pat. No. 1,179,422 and German Auslegeschrift No. 1,271,616).
In these machines the stationary cam is in one plane. When the carrier revolves, the arm following the stationary cam accordingly changes its radial position. On account of the varying radial distance, the arm is thus exposed to varying centrifugal forces. These varying forces apply varying stresses to the cam irrespective of the stresses involved in the acceleration of the pickup members. This results in premature wear at the points of greater stress. This wear, however, manifests itself as free play in the pickup element drive, so that the element is unable to perform its rolling action at the stations with the desired precision.

Labeling machine, especially for bottles


An interface for current mode to Manchester encoded serial signals includes symmetric positive/negative doublet pulse generation. The doublet pulse generator provides a pulse duration held constant by feedback and a voltage/current reference and a minimal gap between the positive and negative polarity pulses by using the same signal to both turn off the positive pulse driver and turn on the negative pulse driver. Fault detection by sensing of asymmetry in received pulse uses a comparator with both high and low threshold input devices to provide minor asymmetry tolerance.

The present invention relates to electronic devices, and, more particularly, to semiconductor devices useful in communications.
Communications aboard an airplane for pilot control of wing flaps, tail flaps, engine power, and so forth were originally by cables running from the cockpit to the various controlled devices. Reliability and complexity of control communications led to the substitution of hydraulic systems for the cable systems, and then the substitution of electrical systems for hydraulic systems. Such electrical systems are constrained by requirements of high reliability and small available space. Multiple backups and noise tolerance help reliability, and balanced differential signals provide maximal noise immunity. Early electrical systems such as ARINC-429 use direct point-to-point wiring. Later systems such as MIL-1553 use a multiplexed serial data bus with Manchester coding to save weight by sharing a common bus cable. The still more recent ARINC-629 standard uses magnetic coupling into a bus cable without piercing the insulation of the cable; this gives higher reliability at the expense of more complex coding. Thus airplane control communication systems need efficient conversion between the usual Manchester encoded serial information and ARINC-629 protocol serial information.
Problems of conversion between Manchester and ARINC-629 signals include the generation of pairs of pulses (for the ARINC-629 doublets) of stable duration, with minimal delay between the input and the pulse pair output, and without the use of a crystal oscillator or system clock due to time delays or space limitations. Also, to increase reliability, the differential receivers need fault detection capability. This includes both the checking of an incoming signal for proper waveshape and the self-diagnosis of faults in he receiver circuitry.
Abidi, 22 IEEE J.Sol.St.Cir. 494 (1987) describes a switched capacitor feedback loop to linearize a voltage controlled oscillator. Wakayama and Abidi, 1987 IEEE Int.Sol.St.Cir.Conf. 220 (1987) describe a voltage controlled oscillator with a frequency to voltage servo loop to linearize the oscillator.
Liu and Meyer, 1988 IEEE Int.Sol.St.Cir.Conf. 22 (1988) use a frequency to voltage converter to linearize a voltage controlled oscillator and include a master slave arrangement to calibrate the oscillator frequency by locking to a crystal reference frequency.
Ware et al, 24 IEEE J.Sol.St.Cir. 1560 (1989) describe a reference voltage and a reference current generated from the same bandgap circuit so the references track and errors cancel.
The present invention provides pulse pair generation with stabilized pulse widths plus abutting opposite polarity portions without reliance on crystal oscillators or clock synchronization plus also provides fault detection within the paired transmission wiring.


A semiconductor protection circuit for protecting an internal circuit from a surge voltage comprises a depletion type PMOS transistor in which its drain is connected to the input/output terminal of the internal circuit. The control terminal is connected to the gate of the PMOS transistor. The control terminal applies a high voltage or low voltage to the gate in accordance with the ON or OFF state of the power of the internal circuit. When the high or low voltage is applied to the gate, the channel of the PMOS transistor is rendered non-conductive or conductive, respectively. The source of the PMOS transistor is grounded via a PN diode and a junction capacitor provided parallel. When a negative surge voltage is applied, the diode is rendered conductive, thereby protecting the internal circuit. When a positive surge voltage is applied, the diode is broken down, thereby protecting the internal circuit. The surge voltage is made small by the capacitor's static capacitance. When the control terminal is in a high potential state, the channel of the PMOS transistor disappears. Therefore, the internal circuit is released from the diode and capacitor. Consequently, during operation of the internal circuit, the parasitic capacitance at the input/output terminal is reduced.

1. Field of the Invention
The present invention relates to a semiconductor protection circuit and a semiconductor protection device, more specifically to those designed to protect a main semiconductor device from a surge voltage applied to an input/output terminal for a high-frequency signal, from outside.
2. Description of the Related Art
Semiconductor protection circuits generally used are shown in FIGS. 1, 2 and 3.
In the circuit shown in FIG. 1, between an internal circuit 100 and its input/output terminal 101, a power source Vcc (maximum potential) is connected via a PN diode 102, and so is a minimum potential (ground) via a Schottkey diode. A cathode of the PN diode 102 is connected to the power source vcc, and an anode is connected to the input/output terminal 101. Further, a cathode of the Schottkey diode 103 is connected to the input/output terminal 101, and an anode is connected to the minimum potential.
According to the circuit shown in FIG. 1, in the case where a positive surge potential higher than Vcc (maximum potential) is applied to the input/output terminal 101, the PN diode 102 is rendered conductive, thereby protecting the internal circuit 100. In the case where a negative surge potential lower than the minimum potential is applied to the input/output terminal 101, the Schottkey diode 103 is rendered conductive, thereby protecting the internal circuit 100.
In the circuit shown in FIG. 2, between an internal circuit 100 and its input/output terminal 101, a power source Vcc (maximum potential) is connected via a PMOS transistor 104, and so is a minimum potential (ground) via an NMOS transistor 105. Drains of both MOS transistors 104 and 105 are connected to the input/output terminal 101, and gates and sources are connected to the power source Vcc or the minimum potential.
According to the circuit shown in FIG. 2, in the case where a positive surge potential is applied to the input/output terminal 101, the PMOS transistor 104 is rendered conductive, thereby protecting the internal circuit. In the case where a negative surge potential is applied to the input/output terminal 101, the NMOS transistor 105 is rendered conductive, thereby protecting the internal circuit 100.
In the circuit shown in FIG. 3, a bipolar transistor is provided in order to help a MOS transistor having a low current driving property. Between an internal circuit 100 and its input/output terminal 101, a power source Vcc (maximum potential) is connected via an NPN bipolar transistor 106, and so is a minimum potential (ground) via a Schottkey diode 103. An emitter of the bipolar transistor 106 is connected to the power source Vcc, and a collector is connected to the input/output terminal. A cathode of the Schottkey diode 103 is connected to the input/output terminal 101, and an anode is connected to the minimum potential. Further, a source of an NMOS transistor 107 is connected to a base of the bipolar transistor 106. A gate and a drain of the MOS transistor 107 are connected to the input/output terminal 101.
According to the circuit shown in FIG. 3, in the case where a positive surge potential is applied to the input/output terminal 101, the NPN bipolar transistor 106 is rendered conductive, thereby protecting the internal circuit. In the case where a negative surge potential is applied to the input/output terminal 101, the Schottkey diode 103 is rendered conductive, thereby protecting the internal circuit 100.
Next, the structure of a conventional semiconductor protection device will be described with reference to FIG. 4. FIG. 4 is a cross sectional view of a structure in which the protection circuit shown in FIG. 3 is realized on a semiconductor substrate, including the NPN bipolar transistor 106 and the NMOS transistor 107.
N-type well layers 114a, 114b and 114c are formed in the surface region of a P-type semiconductor substrate, and an oxide film 115 is selectively formed on the substrate 111 such as to surround these layers. A thin oxide film 117 is formed on the N-type well layers 114a, 114b and 114c so as to protect the surface of the substrate 111.
A polysilicon gate electrode 118 is provided between the N-type well layers 114a and 114b, which serve as the drain and source of the NMOS transistor 107, on the oxide film. High-concentration N-type layers 121 and 120 are formed in the surface regions of the N-type well layers 114a and 114b. Further, a P-type base layer 125 is formed in the surface region of the N-type well layer 114c which serves as the collector of the NPN bipolar transistor 106 and an N-type emitter layer 126 is formed in the surface region of the base layer.
An SiO.sub.2 insulation film 122 is deposited on the substrate 111 by the CVD. Contact holes are formed in the insulation film 122, and electrodes 123 are provided in these holes.
The conventional protection circuits share a common problem in which a large parasitic capacitance of the protection circuit is assigned to the input/output terminal, where high-frequency signals pass by, thus reducing the signal charge. Therefore, in order to achieve a high frequency characteristic, a protection circuit cannot be provided. As a result, the reliability as regards static breakage must be inevitably deteriorated. Apart from this, the conventional circuits entail the drawbacks described below.
FIG. 5 illustrates an example in which two protection circuits such as shown in FIG. 1 are provided on boards B1 and B2, which are connected to each other via a connection cord C. In this case, in the case where the power voltage of the circuit of the board B1 is maintained at the regular operation level while the power of the circuit on the board B2 is turned off, thus reducing the potential at the position L2 to the GND level, the PN diode 102 of the board B2 is turned on since the output voltage of the board B1 (the potential at the position L1 in the figure) is at a high level. Thus, an undesired current flows from the position L1 to the position L2, causing a loss of the signal charge applied to the input/output terminal.
Further, in the case where a bipolar transistor and an MOS transistor are provided as shown in FIG. 4, in order to avoid the damage to circuit due to the concentration of the breakdown current, the area occupied by the entire protection device is increased, raising another drawback in terms of high degree of integration.

Semiconductor protection circuit and semiconductor protection device