Title of Invention: Automatic Cardiac Therapy Advisor With Hidden Markov Model Processing Abstract: A method of automatically determining which type of treatment is most appropriate for (or the physiological state of) a patient. The method comprises transforming one or more time domain measurements from the patient into frequency domain data representative of the frequency content of the time domain measurements; processing the frequency domain data to form a plurality of spectral bands, the content of a spectral band representing the frequency content of the measurements within a frequency band; forming a weighted sum of the content of the spectral bands, with different weighting coefficients applied to at least some of the spectral bands; determining the type of treatment (or physiological state) based on the weighted sum. Detailed Description: CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation application of and claims priority to U.S. application Ser. No. 13/454,061, filed on Apr. 23, 2012, now U.S. Pat. No. 9,339,436, which application is a continuation application of and claims priority to U.S. application Ser. No. 11/136,327, filed on May 24, 2005, now U.S. Pat. No. 8,165,671, which application claims priority to U.S. Provisional Application Ser. No. 60/674,175, filed on Apr. 22, 2005. All applications hereby incorporated by reference. TECHNICAL FIELD This invention relates to techniques for automatically advising as to the appropriate cardiac therapy for a patient, e.g., the particular therapy to be used for cardiac resuscitation. BACKGROUND The heart relies on an organized sequence of electrical impulses in order to beat effectively. Any deviation from this normal sequence is known as “arrhythmia.” A class of devices includes signal processing software that analyzes electrocardiography (ECG) signals acquired from the victim to determine when a cardiac arrhythmia such as ventricular fibrillation (VF) or shockable ventricular tachycardia (VT) exists. These devices include automated external defibrillators (AEDs), ECG rhythm classifiers, or ventricular arrhythmia detectors. An AED is a device that literally “talks” the provider through a process of evaluating a patient for, attaching the patient to, and activating, the AED therapy. This device is capable of recognizing the two distinct cardiac waveforms: VT and VF. VT is a tachydysrhythmia originating from a ventricular ectopic focus, characterized by a rate typically greater than 120 beats per minute and wide QRS complexes. VT may be monomorphic (typically regular rhythm originating from a single focus with identical QRS complexes) or polymorphic (unstable, may be irregular rhythm, with varying QRS complexes). An example rhythm for an unstable VT is illustrated in FIG. 1A. Depending on the rate and the length of time that the VT has been sustained, a heart in the VT state may or may not produce a pulse (i.e., pulsatile movement of blood through the circulatory system). The cardiac activity still has some sense of organization (note that the “loops” are all basically the same size and shape). If there is no pulse associated with this VT rhythm, then the VT is considered to be unstable and a life threatening condition. An unstable VT can be treated with an electrical shock or defibrillation. Supraventricular tachycardia (SVT) is a rapid heartbeat that begins above the hearts lower chambers (the ventricles). SVT is an abnormally fast heart rhythm that begins in one of the upper chambers of the heart (atria), a component of the heart's electrical conduction system called the atrioventricular (AV) node, or both. Although SVT is rarely life-threatening, the symptoms which include a feeling of a racing heart, fluttering or pounding in the chest or extra heartbeats (palpitations), or dizziness can be uncomfortable. VF is usually an immediate life threat. VF is a pulseless arrhythmia with irregular and chaotic electrical activity and ventricular contraction in which the heart immediately loses its ability to function as a pump. VF is the primary cause of sudden cardiac death (SCD). An example rhythm for VF is illustrated in FIG. 1B. This waveform does not have a pulse associated with it. There is no organization to this rhythm (note the irregular size and shape of the loops.) The pumping part of the heart is quivering like a bag of worms, and it is highly unlikely that this activity will move any blood. The corrective action for this rhythm is to defibrillate the heart using an electrical charge. A normal heart beat wave starts at the sinoatrial node (SA node) and progresses toward the far lower corner of the left ventricle. A massive electrical shock to the heart can correct the VF and unstable VT rhythms. This massive electrical shock can force all the cardiac cells in the heart to depolarize at the same time. Subsequently, all of the cardiac cells go into a short resting period. The hope is that the sinoatrial node (SA node) will recover from this shock before any of the other cells, and that the resulting rhythm will be a pulse producing rhythm if not normal sinus rhythm. For AEDs, algorithms to recognize the two waveforms VT and VF are designed to perform ECG analyses at specific times during a rescue event of a patient using defibrillation and cardio-pulmonary resuscitation (CPR). The first ECG analysis is usually initiated within a few seconds following attachment of the defibrillation electrodes to the patient. Subsequent ECG analyses may or may not be initiated based upon the results of the first analysis. Typically, if the first analysis detects a shockable rhythm, the rescuer is advised to deliver a defibrillation shock. Following the shock delivery, a second analysis is automatically initiated to determine whether the defibrillation treatment was successful or not (i.e., the shockable ECG rhythm has been converted to a normal or other non-shockable rhythm). If this second analysis detects the continuing presence of a shockable arrhythmia, the AED advises the user to deliver a second defibrillation treatment. A third ECG analysis may then be initiated to determine whether the second shock was or was not effective. If a shockable rhythm persists, the rescuer is then advised to deliver a third defibrillation treatment. Following the third defibrillator shock or when any of the analyses described above detects a non-shockable rhythm, treatment protocols recommended by the American Heart Association and European Resuscitation Council require the rescuer to check the patient's pulse or to evaluate the patient for signs of circulation. If no pulse or signs of circulation are present, the rescuer is trained to perform CPR on the victim for a period of one or more minutes. The CPR includes rescue breathing and chest compressions. Following this period of CPR, the AED reinitiates a series of up to three additional ECG analyses interspersed with appropriate defibrillation treatments as described above. The sequence of three ECG analyses/defibrillation shocks followed by 1-3 minutes of CPR, continues in a repetitive fashion for as long as the AED's power is turned on and the patient is connected to the AED device. Typically, the AED provides audio prompts to inform the rescuer when analyses are about to begin, what the analysis results were, and when to start and stop the delivery of CPR. One limitation associated with many AEDs is that current automated ECG rhythm analysis methods cannot function with extra noise due to CPR chest compressions. Thus, conventional practice is to interrupt chest compressions while performing ECG rhythm analysis. Long interruptions of chest compressions have been shown to result in higher failure rate of resuscitation. Many studies have reported that the discontinuation of precordial compression can significantly reduce the recovery rate of spontaneous circulation and 24-hour survival rate. These studies include “Adverse effects of interrupting precordial compression during cardiopulmonary resuscitation” by Sato et al. (Critical Care Medicine, Volume 25(5), May 1997, pp 733-736), “Adverse Outcomes of Interrupted Precordial Compression During Automated Defibrillation” by Yu et al. (Circulation, 2002), and” Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients With Out-of-Hospital Cardiac Arrest” by Eftestol et al. (Circulation, 2002). Thus, it is useful to recognize abnormal heart rhythms during chest compressions. There is recent clinical evidence showing that performing chest compressions prior to defibrillation under some circumstances can be beneficial. Specifically, it is clinically beneficial to treat a patient with chest compressions prior to defibrillation if the response times of the medical emergency system result in a delay of more than four minutes such that the patient is in cardiac arrest for more than four minutes. If the response times of the medical emergency system result in a capability to treat the patient in sooner than a four minute delay, it can be better for the patient to be treated with defibrillation first. Methods have been developed to determine from the ECG waveform both whether the patient has been in cardiac arrest for longer than the 4 minutes as well as time independent measures of when the most optimal time is to shock. “Non-invasive monitoring and treatment of subjects in cardiac arrest using ECG parameters predictive of outcome” by Brown and Dzwonczyk (U.S. Pat. No. 5,683,424) describes methods to determine from the ECG waveform whether the patient has been in cardiac arrest for longer than the 4 minutes. “Method and system for predicting the immediate success of a defibrillatory shock during cardiac arrest” (U.S. Pat. No. 6,171,257 by Weil et al.) and “Ventricular Fibrillation Scaling Exponent Can Guide Timing of Defibrillation and Other Therapies” by Menegazzi et al. (2004 American Heart Association, Inc.) describe time independent measures of when the most optimal time is to shock. These algorithms use spectral analysis of the ECG to predict defibrillation shock success in some manner. Current methods utilizing spectral analysis of the ECG for chest compression artifact rejection, defibrillation success prediction, and therapeutic decision-making typically specify a set of parameters in the ECG frequency spectrum to be detected. For example, U.S. Pat. No. 5,683,424 compares a centroid or a median frequency or a peak power frequency from a calculated frequency spectrum of the ECG to thresholds to determine if a defibrillating shock is necessary. These parameters do not uniquely specify the frequency or time domain characteristics. For example, the median frequency of the ECG spectrum for almost all patients in ventricular fibrillation decreases initially then increases again after several minutes, making it difficult to use median frequency to predict how long a patient has been in cardiac arrest. Thus, the patient can have the same median frequency at widely differing durations of cardiac arrest. Using amplitudes of the frequency spectrum of the ECG can be limited because the amplitudes are dependent on both the cardiac electrical output as well as position of the ECG lead electrodes on the patient. Some conventional automated ECG rhythm analysis methods detect VF and other arrhythmic heart rhythms by using spectral analysis of the ECG signals with the assumption that the difference in the power spectrum between ECGs of normal heart rhythms and abnormal rhythms is such that during the abnormal rhythm the ECG is concentrated or mainly sinusoidal in a narrow band of frequencies between 4 and 7 Hz, while in normal rhythm the ECG is a broadband signal with major harmonics up to at least 25 Hz. For example, “Comparison of four techniques for recognition of ventricular fibrillation from the surface” by Clayton et al. (ECG Medical & Biological Engineering & Computing 1993; 31:111-117) and “Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artifacts: a diagnostic system” by Barro et al. (Journal of Biomedical Engineering, 1989, Volume 11) analyze the frequency domain of the ECG to check if the ECG is mainly sinusoidal in the narrow band of frequencies. One problem with these conventional methods is that CPR changes the assumption behind the methods so that VF and other dangerous rhythms cannot be typically detected during chest compressions. Adaptive filters have been used in many studies to remove the artifact due to CPR chest compression from the ECG signal. These studies include “CPR Artifact Removal from Human ECG Using Optimal Multichannel Filtering ” by Aase et al. (IEEE Transactions on Biomedical Engineering, Vol. 47, No. 11, November 2000), “Removal of Cardiopulmonary Resuscitation Artifacts From Human ECG Using an Efficient Matching Pursuit-Like Algorithm” by Husoy et al. (IEEE Transactions on Biomedical Engineering, Vol. 49, No. 11, November 2002), “and US Patent 6,390,996 by Halperin et at (2002). The adaptive filters use compression depth and thoracic impedance as reference signals to estimate the artifacts in the ECG signal. The adaptive filter's parameters are updated by calculating the inverse of a cross-correlation matrix or the auto- and cross-spectra of the signal. The artifacts could be reduced when these adaptive filters were applied. However, there is usually a significant part of the artifact left in the estimated ECG signal. Moreover, the adaptive-filter algorithm sometimes has a high computational complexity. These adaptive filtering methods use the compression depth as the reference signal to remove the chest compression artifact from the ECG signals. This is based on the assumption that the chest compression artifact is correlated with the reference signal (compression depth) and independent of the desired ECG signal. This can be true for an infinitely long ECG signal but the estimated coefficients can be biased if a limited length of the ECG signal is applied. It is also possible that the reference signals (such as the compression depth) can provide only part of the information about the CPR artifact presented in the ECG signal, i.e. the noise-reduction ability of the adaptive filter is limited by its knowledge of the noise. Fitzgibbon et al. in “Determination of the noise source in the electrocardiogram during cardiopulmonary resuscitation” (Critical Care Medicine 2002 Vol. 30, No. 4) reported that the thoracic impedance variation due to ventilation or chest compression has little correlation with the artifact in ECG recording during chest compressions. Fitzgibbon et al. (2002) further suggested that the source of the noise in the signal during chest compressions is the electrode motion and related to the electrode's electrical properties, which makes the relation between the noise and the compression depth more complicated. Thus, the artifact cannot be sufficiently attenuated for satisfactory results with the conventional advisory algorithm for fibrillation detection. One method for evaluating medical tests is to determine a test's ability to correctly detect disease, also known as sensitivity, and the test's ability to avoid labeling normal things as disease, also known as specificity. Ideally, a medical test has 100% sensitivity and 100% specificity. When a medical test is imperfect, sensitivity and specificity are plotted on a graph called a receiver-operator characteristics (ROC) curve. Variables in the medical test can be chosen such that the resulting point of the medical test on the ROC curve is closest to a point with 100% sensitivity and 100% specificity. SUMMARY In general, the invention features automatically determining which of a plurality of possible cardiac interventions should be performed in treatment of a patient. Prior information representative of prior cardiac interventions performed on the patient, and information representative of the patient's reactions to the prior cardiac interventions, are stored, and the information is processed using a hidden Markov model to determine which of a plurality of possible further cardiac interventions should be performed. In preferred implementations, one or more of the following features may be incorporated. The patient's reaction to the further cardiac intervention is sensed; further information representative of the further cardiac intervention, and of the patient's reaction to the further cardiac intervention, is stored; and the prior and further information is processed using a hidden Markov model to determine which of still further cardiac interventions should be performed in further treatment of the patient. The patient is a cardiac arrest victim. In other aspects, the invention features a method of automatically determining which type of treatment is most appropriate for (or the physiological state of) a patient. The method comprises transforming one or more time domain measurements from the patient into frequency domain data representative of the frequency content of the time domain measurements; processing the frequency domain data to form a plurality of spectral bands, the content of a spectral band representing the frequency content of the measurements within a frequency band; forming a weighted sum of the content of the spectral bands, with different weighting coefficients applied to at least some of the spectral bands; determining the type of treatment (or physiological state) based on the weighted sum. In preferred implementations, one or more of the following features may be incorporated. The weighting coefficients may be ones chosen using a regression analysis comparing actual time domain measurements and actual outcome of therapy for a population of patients. The weighting coefficients may have been chosen to improve a correlation between the weighted sum and the outcome of therapy. The weighting coefficients are different for at least two different therapy stages. The therapy may be cardiac resuscitation, and the measurement comprises ECG signals. The measurement may comprise ECG signals, and the therapy stages may comprise at least arrival at patient's side, pre-shock, and post-shock. The therapy stage may be based at least in part on rescuer entered data indicative of the stage of therapy. The therapy stage may be based at least in part on rescuer entered data indicative of at least what drugs have been delivered to the patient. The rescuer entered data may be further indicative of whether the patient has been intubated, and whether an automatic external chest compressor has been used. The determining may comprise comparing the weighted sum to a threshold. The threshold may be different for at least two therapy stages. When the therapy is cardiac resuscitation, and the measurement comprises ECG signals, if the weighted sum exceeds the threshold the type of treatment determined to be appropriate may be delivery of a defibrillation shock. The threshold used when the therapy stage is arrival at the patient's side may be lower than the threshold used for later therapy stages. In other aspects, the invention features a method of automatically determining which type of treatment is most appropriate for a cardiac arrest victim, the method comprising transforming one or more time domain electrocardiogram (ECG) signals into a frequency domain representation comprising a plurality of discrete frequency bands, combining the discrete frequency bands into a plurality of analysis bands, wherein there are fewer analysis bands than discrete frequency bands, determining the content of the analysis bands, and determining the type of treatment based on the content of the analysis bands. In preferred implementations, one or more of the following features may be incorporated. Transforming may comprise the Fourier transform. Transforming may comprise a Wavelet transform. Transforming may comprise a Radon transform. Determining the content of the analysis bands may comprise determining a plurality of values. The content and the plurality of values may be calculated at more than two points in time, and wherein the sequence of plurality of values in time may define a trajectory. The trajectory may be analyzed using estimation and prediction methods. The analysis method may involve use of a recursive filter. The recursive filter may be a Kalman filter. The analysis method may involve use of a Particle Filter. The analysis of the trajectory may be used to predict defibrillation success. The analysis of the trajectory may be used to determine whether it is appropriate to defibrillate or deliver an alternative therapy such as chest compressions, drugs such as epinephrine, constitutive nutrients such as glucose, or other electrical therapy such as pacing. A mathematical transformation may be performed on the trajectory. The transformation may be a projection of the trajectory onto a plane within the parameter space. Image mensuration algorithms may be employed to evaluate the features of the two dimensional projection of the trajectory. The content may comprise at least two parameters descriptive of the content of an analysis band from the analysis bands. Determining the content of an analysis band may comprise quantifying the energy within an analysis band. Quantifying the energy within an analysis band may comprise determining at least one number characterizing the energy of the highest peak within the band. Quantifying the energy with an analysis band may comprise determining an overall or average energy for the band. The invention further comprises analyzing the variation over time of the content of the analysis bands. The bands may be about 0.5 Hz in width. The bands may be of unequal widths such that additional resolution is provided for frequency bands that are of greater importance in the analysis. Frequencies less than 3 Hz may be subdivided into bands whose widths are larger than those in the 6-12 Hz frequency range. Each band may be composed of an aggregation of multiple spectral measurements. Characteristics of the distribution of spectral measurements within the band may include at least one of the following descriptors: mean spectral energy, spectral energy variance, median spectral energy, maximum spectral energy, or minimum spectral energy. In another aspect, the invention features a method of automatically determining which type of treatment is most appropriate for a cardiac arrest victim, the method comprising transforming one or more time domain ECG signals into a frequency domain generally containing a plurality of peaks, processing the frequency domain representation to characterize at least a plurality of the peaks, wherein characterizing a peak comprises determining a plurality of parameters characterizing the peak, and determining the type of treatment based on the parameters characterizing at least some of the peaks. In preferred implementations, one or more of the following features may be incorporated. The invention may further comprise analyzing the variation over time of at least some of the plurality of parameters characterizing at least some of the plurality of peaks. The content and the plurality of values may be calculated at more than two points in time, and wherein the sequence of plurality of values in time may define a trajectory. The trajectory may be analyzed using estimation and prediction methods. The analysis method may involve use of a recursive filter. The recursive filter may be a Kalman filter. The analysis method may involve use of a Particle filter. The analysis of the trajectory may be used to predict defibrillation success. The analysis of the trajectory may be used to determine whether it is appropriate to defibrillate or deliver an alternative therapy such as chest compressions, drugs such as epinephrine, constitutive nutrients such as glucose, or other electrical therapy such as pacing. A mathematical transformation may be performed on the trajectory. The transformation may be a projection of the trajectory onto a plane within the parameter space. Image mensuration algorithms may be employed to evaluate the features of the two dimensional projection of the trajectory. Analyzing the variation over time may comprise determining variation in the frequency of a peak. Determining a plurality of parameters characterizing the peak may comprise estimating a shape of the peak. Estimating a shape of the peak may comprise using a non-linear curve fitting routine. The plurality of the peaks may comprise a largest amplitude frequency peak and peaks having a fraction of an amplitude of the largest amplitude frequency peak. The parameters may comprise a frequency of a peak, an amplitude of the peak, and a width of the peak. The parameters may comprise a depth of the peak. The parameters may comprise a variance of the peak. The parameters may comprise a first moment of the peak. The invention further comprises determining a reference frequency from the frequency domain and determining a variance of the energy of the frequency domain using the reference frequency. The invention may also further comprise determining that the victim is in a sinus arrhythmic state if the variance of the energy of the frequency representation is below a threshold. The reference frequency is one of a mean frequency, a median frequency, a center frequency, and a peak frequency. Determining the type of treatment may comprise determining that the type of treatment is to defibrillate the victim's heart if the following conditions are met: the victim is determined to be in the sinus arrhythmic state, a frequency of a largest amplitude frequency peak is less than a first threshold, and the number of peaks is less than a second threshold. Determining the type of treatment may comprise determining that the type of treatment is chest compressions to the victim if the following conditions are met: the victim is determined to be in the sinus arrhythmic state and a frequency of a largest amplitude frequency peak is greater than a first threshold and if the number of peaks is less than a second threshold. Determining the type of treatment may comprise determining that the type of treatment is monitoring the victim or drug therapy if the following conditions are met: the victim is determined to be in the sinus arrhythmic state, and the number of peaks is greater than a threshold. Determining parameters may comprise measuring a change of one or more parameters of the peaks in a range of the frequency spectrum over multiple digital time samples. Each peak may be considered to retain an identity over the multiple digital time samples if its amplitude and frequency do not change more than a threshold from one time sample to a subsequent time sample. Determining the type of treatment may comprise comparing an oscillation cycle rate of the change to a cycle rate band and if the cycle rate is in the band, determining that the type of treatment is to defibrillate the victim's heart. Determining the type of treatment may further comprise determining that a defibrillating shock to the victim's heart is suitable therapy when the oscillation is at or near a maximum. For two or more peaks the change may be a relative decrease, and wherein determining the type of treatment may comprise comparing the relative decrease to a threshold, and if the relative decrease is above the threshold, the type of treatment may be chest compressions and then defibrillation. The one or more parameters may comprise amplitudes of the peaks, the threshold may be about fifteen percent, and the multiple digital time samples may cover at least a ten second interval. For two or more peaks, the change may be a relative increase, the parameters may comprise frequency of the peaks, amplitude of the peaks, or width of the peaks, and wherein determining the type of treatment may comprise comparing the relative increase to a threshold, and if the relative increase is above the threshold, the type of treatment may be defibrillation. For two or more peaks, the change may be a decrease, and the parameters may comprise variance of the frequency of the peaks, and wherein determining the type of treatment may comprise comparing the decrease to a threshold, and if the decrease is below a threshold, the type of treatment may be defibrillation. The range of the frequency spectrum may be six to twelve Hertz. The invention may further comprise communicating the type of treatment to one of a drug infusion device, a portable chest compression device, and a ventilator. The invention may also further comprise displaying an indication of the type of treatment. Displaying an indication of the type of treatment may comprise displaying a value of an estimation of an accuracy of the type. In another aspect, the invention features a method of automatically determining which type of treatment is most appropriate for a cardiac arrest victim, the method comprising transforming one or more time domain ECG signals into a frequency domain representation, processing the frequency domain representation to characterize the content of the frequency domain representation in a band from about 6 to about 12 Hz, and determining the type of treatment based on the content in the band. In preferred implementations, one or more of the following features may be incorporated. The invention may further comprise relying on a ventricular fibrillation (VF) or a ventricular tachycardia (VT) advisory algorithm to determine whether the victim is in VF or VT, and wherein determining the type of treatment may comprise determining when to deliver a shock. The content in the band may comprise a quantitative measure representative of approximately the total energy in the band. In another aspect, the invention features a method of automatically determining which type of treatment is appropriate for a cardiac arrest victim, the method comprising measuring at least one physiological signal, determining at least two parameters related to the at least one physiological signal, the at least two parameters forming a parameter set, repeating the measurement and calculation at more than two points in time to create a sequence of parameter sets, wherein the sequence of parameter sets defines a trajectory, and analyzing the trajectory using estimation and prediction methods that comprise the use of a recursive filter. In preferred implementations, one or more of the following features may be incorporated. The recursive filter may be a Kalman filter. The analysis method may involve use of a Particle filter. The analysis of the trajectory may be used to predict defibrillation success. The analysis of the trajectory may be used to determine whether it is appropriate to defibrillate or deliver an alternative therapy such as chest compressions, drugs such as epinephrine, constitutive nutrients such as glucose, or other electrical therapy such as pacing. A mathematical transformation may be performed on the trajectory. The transformation may be a projection of the trajectory onto a plane within the parameter space. Image mensuration algorithms may be employed to evaluate the features of the two dimensional projection of the trajectory. The predicted next state of the parameter set may be used to determine the appropriate treatment. The method may be carried out by a device configured to determine an appropriate therapy for a rescuer to perform on the victim. The probability of defibrillation success associated with a plurality of alternative treatments may be shown on the display of the device. The probability of success with a plurality of treatments may be shown on the display as range of numbers. The device may be an AED that notifies the rescuer in the form of an audible or visual alarm indicating that the paramedic should stop doing compressions for a more accurate analysis of the ECG waveform. The device may be an AED that notifies the rescuer in the form of an audible or visual alarm indicating that the paramedic should alter the therapy being delivered. In another aspect, the invention features an AED capable of automatically determining which type of treatment is appropriate for a cardiac arrest victim, the AED comprising electrical therapy pads configured to deliver electrical therapy to patients, ECG electrodes smaller in diameter than the electrical therapy pads are integrated into the electrical therapy pads, the smaller ECG electrodes are configured to provide at least one additional electrical vector that is approximately orthogonal to the monitoring vector produced by the ECG signal from the therapy electrodes. In preferred implementations, one or more of the following features may be incorporated. A vector sum of the at least one additional electrical vector and the monitoring vector may provide a trajectory over time that can be used by the AED in determining which type of treatment is appropriate. These and other implementations may have one or more of the following advantages. The method uses frequency-domain analysis methods for ECG processing/advisory during chest compressions. This method allows ECG analysis without interruption of chest compression and thus significantly reduces the interruption time during chest compressions, leading to an increase in the success rate of resuscitation. Some implementations allow for the more complete specification of the ECG waveform spectrum for different cardiac states in a mathematically tractable form that provides improved receiver-operator characteristics (ROC) of the detection algorithm, while reducing the performance burden on the processor. Other features and advantages of the invention will be apparent from the description and drawings, and from the claims. DESCRIPTION OF DRAWINGS FIG. 1A is a magnitude versus time plot of a ventricular tachycardia (VT) rhythm. FIG. 1B is a magnitude versus time plot of a ventricular fibrillation (VF) rhythm. FIG. 2 is a diagram of one implementation including an automatic electronic defibrillator (AED) and a multiple lead electrocardiograph (ECG) device. FIG. 2A is a diagram of the AED of FIG. 2. FIG. 3A is an example of a frequency spectrum plot with the energy concentrated within a small frequency range. FIG. 3B is an example of a frequency spectrum plot with the energy distributed over a relatively larger frequency range. FIG. 4 is an example of an ECG spectrum as a function of time. The magnitude (or energy) of the spectrum is encoded by the grayscale. A darker color corresponds to a higher magnitude. FIG. 5 is an EFV score of the signal in FIG. 4 as a function of time. FIG. 6 is a flow chart of a process for detecting VF in a patient during chest compressions. FIG. 7A and 7B are examples of an ECG spectrum at two points in time, in this case separated by 4 seconds. FIG. 8 shows a logistic curve that relates a measured predictive variable (x axis) into a approximate probability of therapeutic success (y axis). DETAILED DESCRIPTION There are a great many different implementations of the invention possible, too many to possibly describe herein. Some possible implementations that are presently preferred are described below. It cannot be emphasized too strongly, however, that these are descriptions of implementations of the invention, and not descriptions of the invention, which is not limited to the detailed implementations described in this section but is described in broader terms in the claims. Referring to FIG. 2, a rescuer uses an AED 10 to automatically monitor a victim during cardiac resuscitation. The AED 10 includes a speaker 16, a display screen 18, an analog to digital converter 20, a processor 22, and a defibrillator pulse generator 24. The analog-to-digital converter 20 is connected to a set of ECG leads attached to the victim. The ECG leads monitor the electrical rhythms of the victim's heart. The converter 20 sends the signals from the ECG leads to the processor 22. The processor 22 monitors the victim's heart for dangerous rhythms using the ECG signals while the victim is resuscitated using chest compressions techniques. If the AED 10 detects a dangerous heart rhythm, the AED 10 generates an alarm signal. The alarm signal is noticeable to the rescuer. The AED 10 can generate a defibrillating shock to the victim when the rescuer issues a command to the AED 10. The defibrillating shock is intended to remedy the dangerous rhythm of the victim's heart. The AED 10 uses a rhythm advisory method for a) quantifying the frequency-domain features of the ECG signals; b) differentiating normal and abnormal ECG rhythms, such as VF; c) detecting the onset of abnormal ECG rhythms; and d) making decisions about the physiological states of the heart. This frequency-domain measure is reliable with or without the presence of the chest compression artifact in the ECG signals. The AED 10, after identifying the current physiological state of the heart, can make a decision about appropriate therapeutic action for the rescuer to make and communicates the action to the rescuer using the speaker 16 and the display screen 18. This rhythm advisory method can also be incorporated in an ECG rhythm classifier or a ventricular arrhythmia detector. The AED 10 may incorporate functionality for performing additional therapeutic actions such as chest compressions, ventilations, or delivery of intravenous solution containing metabolic or constitutive nutrients. Based on the results of the analysis of the rhythm advisory method, the AED 10 may automatically deliver the appropriate therapy to the patient. The AED 10 may also be configured in “advisory” mode wherein the AED 10 will prompt the caregiver after the AED 10 has made a determination of the best therapy, and acknowledgement by the caregiver/device operator, in the form of a button press or voice-detected acknowledgement, is required before therapy is delivered to the patient. The AED 10 then analyzes the ECG signals to predict defibrillation success as well as to decide whether it is appropriate to defibrillate or to deliver an alternative therapy such as chest compressions, drugs such as epinephrine, constitutive nutrients such as glucose, or other electrical therapy such as pacing. In some examples, one or more therapeutic delivery devices 30 automatically deliver the appropriate therapy to the patient. The therapeutic delivery devices 30 are physically separate from the defibrillator AED 10 and control of the therapeutic delivery devices 30 may be accomplished by a communications link 32. The communications link 32 may take the form of a cable connecting the devices 10, 30, but preferably the link 32 is via a wireless protocol such as Bluetooth or a wireless network protocol such as Institute of Electrical and Electronics Engineers (IEEE) 802.11. Bluetooth is a telecommunications industry specification that describes how mobile computing devices can be interconnected using a short-range wireless connection. The therapeutic delivery device 30 can be a portable chest compression device that is commercially available as the Autopulse™, provided by Revivant of Sunnyvale, California. In other examples, the therapeutic delivery device 30 is a drug infusion device that is commercially available as the Power Infuser™, provided by Infusion Dynamics of Plymouth Meeting, Pennsylvania, or the Colleague CX™, provided by Baxter Healthcare Corp., of Round Lake, Ill. The therapeutic delivery device 30 can be a ventilator that is commercially available as the iVent™, provided by Versamed of Pearl River, N.Y. The therapeutic delivery device 30 can also include multiple therapies such as defibrillation, chest compression, ventilation and drug infusion. In other examples, control and coordination for the overall resuscitation event and the delivery of the various therapies may be accomplished by a device 34 or processing element external to the AED 10, for instance the device 34 may download and process the ECG data from the AED 10; analyze the ECG signals, perform the determinations based on the analysis, and control the other therapeutic devices 30, including the AED 10. In other examples, the AED 10 may perform all the processing of the ECG, including analyzing the ECG signals, and transmit to the control device 34 only the final determination of the appropriate therapy, whereupon the control device 34 would perform the control actions on the other linked devices 30. The control device 34 is commercially available as the Autopulse™, provided by Revivant of Sunnyvale ,Calif. The chest compression artifact can be separated from the ECG signal components in the frequency domain. This makes it possible for the AED 10 to process the ECG signal without halting the processing during CPR chest compressions. The compression rate during CPR chest compressions recommended by American Heart Association (2000) is 100 per minute or 1.7 Hz and the frequency range used for quantifying the frequency-domain features of the ECG signals can be set to be higher than that (preferably but not limited to be 3 Hz and up) using a high pass frequency filter. The rhythm advisory method quantifies the energy distribution of the ECG signal in the frequency domain with a quantification method. The quantification result can be used to differentiate normal and dangerous ECG rhythms with or without the presence of the chest compression artifact. In one method, the AED 10 breaks up the frequency domain of the ECG signal into analysis frequency bands. The AED 10 then analyzes the different frequency bands for energy or variation over time to determine an appropriate treatment for the victim. In the preferred embodiment, the bands are 0.5 Hz in width, though they may also be divided into unequal widths such that additional resolution is provided for frequency bands that are of greater importance in the analysis. For instance, frequencies less than 3 Hz may be subdivided into only three equally spaced bands while the range from 3-5 Hz may have 0.5 Hz bands, and the range of 6-12 Hz may have 0.25 Hz bands. Each band may be composed of an aggregation of multiple spectral measurements. For each band, characteristics of the distribution of spectral measurements within the band mayinclude such descriptors, e.g., as mean spectral energy, spectral energy variance, median spectral energy, maximum spectral energy, minimum spectral energy. In one example of the analysis frequency bands, the AED 10 generates the frequency bands based on peaks in the frequency spectrum. Thus, one frequency band corresponds to the frequency spread of a given peak in the frequency spectrum. There are common algorithms for identifying peaks in the frequency spectrum that include calculating slopes and energy at different points of the frequency spectrum. For each of these peaks, the AED 10 uses a non-linear parameter estimation algorithm or curve fitting algorithm to estimate the shape of the peak. From this spectral shape, the AED 10 calculates parameters about the peak. The quantification method differentiates various spectral patterns and shapes. The AED 10 makes a decision about the physiological state of the heart and suitable therapy based on the quantification results. The quantification method of the rhythm advisory method is a combination of measures from sub-methods. Some of these sub-methods differentiate various spectral shapes, including but not limited to: (1) the number of peaks in the target frequency range, (2) the relative strength/peak value of various spectral peaks, (3) the relative bandwidth of various spectral peaks and (4) the variance of the energy distributed in a selected frequency range. One or more sub-methods can also measure change in the spectral information over time. These measures can be combined in a multi-dimension space to enhance both the sensitivity and specificity of the decision. One or more information processing techniques can be used to quantify the combination following the computation of these measures in order to make a decision based on the combination. The information processing techniques can include but are not limited to simple combining rules or math, neural networks, expert systems incorporating fuzzy or standard logic, or other artificial intelligence techniques. The additional measures can also include measurement of velocity or acceleration of chest compression during chest compressions according to the techniques taught by U.S. application Ser. No. 10/704,366, Method and Apparatus for Enhancement of Chest Compressions During Chest Compressions, filed on Nov. 6, 2003. The information processing techniques include simple combining rules or math, neural networks, expert systems incorporating fuzzy or standard logic, or other artificial intelligence techniques. These techniques make a decision based on the combination of measures about the physiological state of the heart and suitable therapy. The different measures are individual indications that have varying degrees of uncertainty about the physiological state of the heart and suitable therapy. In some examples, the information processing technique is trained automatically using software techniques known to those skilled in this art and a database of ECG rhythms that include outcome data. These examples include neural networks. In other examples, the information processing technique is generated manually based on observations of ECG patterns and outcomes. These examples include simple combining rules or math, and expert systems utilizing fuzzy or standard logic. In the example of expert systems utilizing standard logic, a programmer manually generates logical rules without uncertainty, the rules specifying preconditions such as “if measure A recommends defibrillation” and “if measure B recommends defibrillation”, and if these preconditions are met, the AED 10 automatically defibrillates the patient. In the example of expert systems utilizing fuzzy logic, the rules are more “fuzzy” and the states to be combined incorporate some degree of uncertainty based on human language. For instance, the fuzzy logic rules can incorporate such input as “measure A detects a strong need for defibrillation” versus “measure A detects a weak need for defibrillation”. The fuzzy logic framework combines the different measures and outputs results such as “strong need for defibrillation” or “weak need for defibrillation”. The method of making the decision about the physiological state is to choose from a group of possible states, each of which corresponds to a predetermined value range of the proposed measure. The possible states can include but are not limited to normal sinus rhythm, VF, shockable (unstable) VT, stable VT, supraventricular rhythm, and pulseless electrical activity. One possible sub-method for the quantification method is the variance of the energy distributed in a selected frequency range, or variance sub-method. Two examples of energy-distribution patterns are shown in FIGS. 3A and 3B. The frequency spectrum plots of FIGS. 3A and 3B are calculated using a fast Fourier transform (FFT) of a signal over time. Referring to FIG. 3A, the energy Y1(f) of a frequency spectrum 50 is concentrated within a narrow frequency band and represents a pattern found in an arrhythmic state such as VF. Referring to FIG. 3B, the energy Y2(f) of a frequency spectrum 52 is distributed over a wide frequency range and represents a pattern found in a non-dangerous heart rhythm or normal sinus rhythm. The variance sub-method quantifies the features of the two frequency spectra 50, 52 and thus the variance sub-method can differentiate between an arrhythmic state and normal sinus rhythm. One example of the variance sub-method calculates the variance of the energy from a reference frequency (Fref) of the spectrum. Possible candidates of the reference frequency include but are not limited to the mean frequency, the median frequency, the center frequency, or the peak frequency of the spectrum. In this example, the variance sub-method computes the weighted distance of each frequency component from the reference frequency of the spectrum and thus quantifies the energy-distribution pattern. An example of this measure, the energy-frequency variance (EFV) can be calculated with the following mathematical equation: EFV = ∫ ( f - F ref ) 2 × Y  ( f )   f ∫ Y  ( f )   f However, the variance sub-method is not limited to this mathematical equation. Measures that quantify the weighted or un-weighted distance of the frequency components from a reference frequency of the frequency spectrum can be used for this measure. Referring to FIG. 3A, energy of the spectrum 50 is concentrated within a narrow frequency range and thus the spectrum has a relatively small EFV value. Referring to FIG. 3B, energy of the spectrum 52 is distributed over a relatively wider frequency range and the spectrum has a relatively larger EFV value. Thus, the EFV value can be used to distinguish between a normal sinus rhythm and an arrhythmic sinus rhythm (e.g., VF). Referring to FIG. 4, a spectrum 100 of a piece of an ECG signal is a function of time. Part 102 of the signal shows a VF rhythm during chest compressions. Part 104 of the signal shows a VF rhythm without chest compressions. The VF is terminated by an electrical shock 106, which is followed by a period of normal sinus rhythm (NSR). During this NSR period, part 108 has no chest compressions while part 110 has chest compressions. Chest compression artifacts that are characterized by strong low-frequency (below 3 Hz) components can be observed in the first 15 seconds (part 102) and the last 10 seconds (part 110) of this time-frequency plot 100. During the time periods 102 and 104 that are associated with VF (i.e. before the electrical shock 106), the energy distribution Y(f) above 4 Hz is clearly concentrated in a small frequency range, with or without the presence of the chest compression artifact. During the time periods 108 and 110 of NSR (i.e. after the electrical shock 106), the energy distribution Y(f) above 4 Hz has a pattern that the energy is distributed over a wide frequency range, with or without the presence of the chest compression artifact. Referring to FIG. 5, an EFV score 152 is calculated from the signal 100 (shown in FIG. 4). A threshold 154 can be used to distinguish an arrhythmic rhythm from a normal sinus rhythm. Thus, during the first 50 seconds (parts 102 and 104 having VF rhythm) of the signal 100, the EFV score 152 is below the threshold 154. Referring to FIG. 6, a variance sub-method 200 is implemented in the software and/or hardware of the AED 10. The ECG data acquired by the front-end analog to digital converter 10 of the AED 10 is processed in a segment-by-segment manner. The number of segments to be processed before a decision is made is predetermined (e.g., 9 segments). The length of a segment is preferably 2 seconds and each segment preferably has a 1-second overlap with both the segment before and after itself, for the desired frequency and time-domain resolution. The segment-counter is set (202) to be zero when the processing starts and the first segment of the signal is acquired (204). A high-pass filter with a desired cutoff frequency (preferably but not limited to be 0.5 Hz) is then applied (206) to remove the baseline drift. The frequency-domain representation of the filtered signal is acquired via a fast fourier transform (FFT) (208). The spectral shape is quantified (210) using a preferred method. In an example, the EFV score is calculated based on this frequency-domain representation and the frequency range for the EFV calculation is selected such that the low-frequency part where the chest compression artifact dominates is excluded. The segment counter is increased (212) by one after the quantification of the spectral shape. If (214) all of the predetermined
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