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EEG-Biofeedback and Epilepsy: Concept, Methodology and Tools for (Neuro)therapy Planning and Objective Evaluation
- Sheena Heath
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1 EEG-Biofeedback and Epilepsy: Concept, Methodology and Tools for (Neuro)therapy Planning and Objective Evaluation Dissertation zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.) vorgelegt der Fakultät für Informatik und Automatisierung der Technischen Universität Ilmenau von Mehmet Eylem Kirlangic, M.S. Tag der wissenschaftlichen Aussprache: Gutachter: Prof. Dr.-Ing. habil. G. Henning 2. Gutachter: Doz. Dr. med. habil. R. Both 3. Gutachter: Assoc. Prof. Dr. Y. Denizhan urn:nbn:de:gbv:ilm
2 Acknowledgment You re sure you want walk through this wall with me? You know, for me it s easy. Whatever happens, I am coming home. But you are leaving home. 'True journey is return ' 1, Shevek (Dr.) 2 It was a hard decision for a long journey at the beginning: Choice between paths Uncertainties Insecurities Finally, the decision was made; and probably, the more challenging path was taken. The journey itself was not easier than the decision: Problems with a new language, a new culture, a new climate and, certainly not the least, with all the repetitive bureaucratic procedures, which I had to face, often alone, far away from home. The main motivation that helped be overcome all these difficulties was my fascination with brain research. And now, the only thing I feel is the satisfaction of being able to present the results of my work. There are several people to whom I am thankful for making my journey a very pleasant one, despite the difficulties. I would like to thank Prof. Günter Henning, my supervisor, for accepting me as his Ph.D. student and for his support, tolerance and trust in me all through my studies. I would like to thank Dr. Galina Ivanova, the leader of the NeuroCybernetics Research Group, not only for her commitment and belief in our project, but also for her encouragement, supervision and scientific competence. It is an ineffable pleasure for me to have contributed to her project, which has been awarded with the Innovation Award 2004 for Biomedical Engineering given by the Stiftung Familie Klee. I am sure she will continue to open further frontiers in the field. I am thankful to Doz. Dr. med. habil. Reinhard Both, the head physician of the Neurology Clinic of the Zentral Klinik Bad Berka, and to Prof. Diethard Müller of the Neurology Praxis Ilmenau, for the support and consultations in medical aspects of my work. Without their cooperation and contributions, the clinical applications would not have been possible. There have been several interesting observations throughout my work, which lead me to concepts such as complexity, cooperation, self-organization, and eventually to synergetics. I give very special thanks to Prof. Hermann Haken, the founder of synergetics, for the time he had for me and for the consultations and insightful discussions. His supervision helped me clarify and formulate my ideas. I had the opportunity to present partial results of my work in various conferences. Hence, I could discuss my findings with the international community and face further challenges. I would especially like to thank the organizers of the International Non-linear Sciences Conference 1 Ursula Le Guin, The Dispossessed: An Ambiguous Utopia, p. 319, HaperCollinsPublishers, London, The main character in the a.m. book who is a scientist and leaves his homeplanet (Anarres) to go to another one (Urras) for his research.
3 Acknowledgment ii (INSC2003) in Vienna and the contributors for the fruitful discussions. Special thanks go to the organizing committee of the 25 th International Epilepsy Conference (IEC) 2003, in Lisbon, for the Conference Attendance Award, without which I would not have had the opportunity to share and discuss my findings with the medical community. I am grateful to Prof. Yagmur Denizhan, who introduced me to chaos theory during my M.S. studies at the Bogazici University and guided me with her comments and remarks not only at the INSC2003 conference, but also in our other meetings at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2000), and the International Conference of the IEEE Engineering in Biology and Medicine Society (EMBS2001) in Istanbul. Doz. Dr. Rainer Knauf read the last version of my manuscript. He made comments and suggestions at several points. I admire his supervision and thank him for his very helpful remarks. The NeuroCybernetics Research Group taught me to work in a team with mutual support and assistance. I thank all the members of the group for their contribution to our team spirit. I acknowledge the support of the Deutscher Akademischer Austauschdienst (DAAD German Academic Exchange Service). Without this financial support, I would not have had the opportunity to overtake the current topic as a Ph.D. thesis. I also appreciate the work and organization of the Akademisches Auslandsamt (Foreign Students Office) of the Technische Universität Ilmenau. Despite all the distance, my family was always by my side. Without them, their support and trust in me, I would not have had the courage for this long journey. I would also like to thank Mr. Arno Schewski, without whom the journey would not have even started, for his encouragement, motivation and support. Last but not the least, I would like to thank all the very nice friends I have gained in Ilmenau ( da ist der Himmel blau 3, although it is very often grau 4 ) and in Germany for their hospitality and openmindedness. They all made my journey adventurous and very pleasant. Thank you! The journey is now coming to an end, opening new paths and new challenges not only scientifically but also personally. New decisions are waiting to be made. Surely, they will not be easier than the ones at the beginning. Maybe further journeys are ahead, who knows, maybe, the true journey is, indeed, return Ilmenau, June 2004 Mehmet Eylem Kirlangic 3 Where the sky is blue, a common description in German used for Ilmenau. 4 The color gray in German.
4 Abstract Objective diagnosis and therapy evaluation are still challenging tasks for many neurological disorders. This is highly related to the diversity of cases and the variety of treatment modalities available. Especially in the case of epilepsy, which is a complex disorder not well-explained at the biochemical and physiological levels, there is the need for investigations for novel features, which can be extracted and quantified from electrophysiological signals in clinical practice. Neurotherapy is a complementary treatment applied in various disorders of the central nervous system, including epilepsy. The method is subsumed under behavioral medicine and is considered an operant conditioning in psychological terms. Although the application areas of this promising unconventional approach are rapidly increasing, the method is strongly debated, since the neurophysiological underpinnings of the process are not yet well understood. Therefore, verification of the efficacy of the treatment is one of the core issues in this field of research. Considering the diversity in epilepsy and its various treatment modalities, a concept and a methodology were developed in this work for increasing objectivity in diagnosis and therapy evaluation. The approach can also fulfill the requirement of patient-specific neurotherapy planning. Neuroprofile is introduced as a tool for defining a structured set of quantifiable measures which can be extracted from electrophysiological signals. A set of novel quantitative features (i.e., percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, and hyperventilation heart rate index) were defined, and the methods were introduced for extracting them. A software concept and the corresponding tools (i.e., the neuroprofile extraction module and a database) were developed as a basis for automation to support the methodology. The features introduced were investigated through real data, which were acquired both in laboratory studies with voluntary control subjects and in clinical applications with epilepsy patients. The results indicate the usefulness of the introduced measures and possible benefits of integrating the indices obtained from electroencephalogram (EEG) and electrocardiogram for diagnosis and therapy evaluation. The applicability of the methodology was demonstrated on sample cases for therapy evaluation. Based on the insights gained through the work, synergetics was proposed as a theoretical framework for comprehending neurotherapy as a complex process of learning. Furthermore, direct current (DC)-level in EEG was hypothesized to be an order parameter of the brain complex open system. For future research in this field, investigation of the interactions between higher cognitive functions and the autonomous nervous system was proposed. Keywords: EEG-biofeedback, epilepsy, neurotherapy, slow cortical potentials, objective diagnosis, therapy evaluation, epileptic pattern quantification, fractal dimension, contingent negative variation, hyperventilation, DC-shifts, instantaneous heart rate, neuroprofile, database system, synergetics.
5 Zusammenfassung (Abstract in German) Die Epilepsie ist eine komplexe neurologische Erkrankung, die auf biochemischer und physiologischer Ebene nicht ausreichend geklärt ist. Die Vielfalt der epileptischen Krankheitsbilder und der Behandlungsmodalitäten verursacht ein Defizit an quantitativen Kenngrößen auf elektrophysiologischer Basis, die die Objektivität und die Effizienz der Diagnose und der Therapieevaluierung signifikant erhöhen können. Die Neurotherapie (bzw. EEG-Biofeedback) ist eine komplementäre Behandlung, die bei Erkrankungen, welche in Zusammenhang mit Regulationsproblemen des Zentralnervensystems stehen, angewandt wird. Obwohl sich die Applikationen dieser unkonventionellen Methode erweitern, wird sie nach wie vor stark diskutiert, da deren neuro- und psychophysiologischen Mechanismen wenig erforscht sind. Aus diesem Grund ist die Ermittlung von Kenngrößen als elektrophysiologische Korrelaten der ablaufenden Prozesse zur objektiven Einstellung und Therapievalidierung eines der Kernprobleme des Forschungsgebietes und auch der vorliegenden Arbeit. Unter Berücksichtigung der aktuellen neurologischen Erkenntnisse und der durch Untersuchungen an Probanden, sowie an Epilepsie-Patienten gewonnenen Ergebnisse, wurden ein Konzept und eine Methodologie entwickelt, um die Objektivität in der Diagnose und Therapieevaluierung zu erhöhen. Die Methodologie basiert auf einem Neuroprofil, welches als ein signalanalytisches mehrdimensionales Modell eingeführt wurde. Es beschreibt einen strukturierten Satz quantifizierbarer Kenngrößen, die aus dem Elektroenzephalogramm (EEG), den ereignisbezogenen Potentialen und dem Elektrokardiogramm extrahiert werden können. Als Komponenten des Neuroprofils wurden neuartige quantitative Kenngrößen (percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, hyperventilation heart rate index) definiert und die Methoden zu deren Berechnung algorithmisiert. Die Anwendbarkeit der Methodologie wurde beispielhaft für die Evaluierung von Neurotherapien an Epilepsie-Patienten demonstriert. Als Basis für eine zukünftige Automatisierung wurden ein Softwarekonzept und entsprechende Tools (neuroprofile extraction module und die Datenbank NeuroBase ) entwickelt. Der Ansatz erfüllt auch die Anforderungen der patientenspezifischen Therapieplanung und kann auf andere Krankheitsbilder übertragen werden. Durch die neu gewonnenen Erkenntnisse wurde die Synergetik als ein theoretischer Rahmen für die Analyse der Neurotherapie als komplexer Lernprozess vorgeschlagen. Es wurde die Hypothese aufgestellt, dass das Gleichspannungsniveau im EEG ein Ordnungsparameter des Gehirn ist, wobei das Gehirn als ein komplexes offenes System betrachtet wird. Für zukünftige Forschungen auf dem Gebiet wird empfohlen, die Wechselwirkungen zwischen den höheren kognitiven Funktionen und dem autonomen Nervensystem in diesem Kontext zu untersuchen. Schlüsselwörter: EEG-Biofeedback, Epilepsie, Neurotherapie, langsame kortikale Potentiale, objektive Diagnose, Therapieevaluierung, epileptische Musterquantifizierung, Fraktal Dimension, Kontingent negative Variation, Hyperventilation, DC-Verschiebung, momentane Herzschlagfrequenz, Neuroprofil, Datenbanksystem, Synergetik.
6 Özet (Abstract in Turkish) Birçok nörolojik bozuklukta, vak aların ve tedavi yöntemlerinin çeşitliliği, nesnel tanı ve tedavi değerlendirmelerini güçleştirmektedir. Özellikle epilepsi gibi, gerek biyokimyasal gerekse fizyolojik düzlemlerde yeterince açıklanamamış rahatsızlıklarda, elektrofizyolojik sinyallerden elde edilebilecek yeni nesnel parametrelere ihtiyaç duyulmaktadır. Davranışsal tedavi olarak sınıflandırılıp psikoloji terimiyle aletli koşullanma olarak tanımlanan nöroterapi, epilepsi de dahil olmak üzere merkezi sinir sisteminin regülasyon bozukluklarında kullanılan bir yöntemdir. Uygulama alanları giderek yaygınlık kazanmakla birlikte, nörofizyolojik temelleri henüz açıklık kazanmadığından, bu yöntemin geçerliliği büyük ölçüde tartışılmaktadır. Dolayısıyla, nöroterapinin etkinliğinin saptanması bu araştırma alanının önemli konularındandır. Bu çalışmada, epilepsi hastalığının ve tedavi yöntemlerinin çeşitliliği göz önünde tutularak, tanı ve tedavi değerlendirmelerinde nesnelliği artırmak amacıyla yöntem ve araçlar geliştirilmiş, yeni parametreler tanımlanmıştır. Elektorfizyolojik sinyallerden elde edilebilecek nicelikler kümesini içeren matematiksel bir araç olarak Nöroprofil kavramı tanımlanmıştır. Nöroprofilin bileşenlerinin bir alt kümesi olarak yeni parametreler (epileptik örüntü sıklığı, bağıl negatif değişim düzeyi ölçüsü, hiperventilasyonda doğru akım değişim göstergesi, hiperventilasyonda nabız değişim oranı ve nabız göstergesi) ve bu parametrelerin hesaplanma yöntemleri ortaya konulmuştur. Geliştirilen yöntemlerin otomasyonunu sağlayabilecek yazılım araçları (nöroprofil çıkarım modülü ve veri tabanı) geliştirilmiştir. Tanımlanan parametreler, gerek epilepsi hastalarından gerekse gönüllü katılımcılardan elde edilen sinyallerle hesaplanmış ve karşılaştırılımıştır. Sonuçlar, bu parametrelerin tanı amaçlı kullanılabilirliğini göstermektedir. Özellikle hiperventilasyon sonucu EEG ve EKG sinyallerinde meydana gelen degişimleri niceliklendirmek için tanımladığımız göstergelerin birleşik analizi yeni bir yöntem olarak önemli sonuçlar vermiştir. Geliştirilen yöntemler, tedavi değerlendirilmesi amaçlı örnek vak alarda kullanılmıştır. Çalışmalarda elde edilen gözlem ve sonuçlar doğrultusunda, karmaşık bir öğrenme süreci olarak nöroterapinin daha iyi anlaşılabilmesi için yeni bir düşünce sistematiğininin gerekliliği tartışılmıştır. Sinerjetik yaklaşımın bu alanda teorik bir çerçeve sağladığı öne sürülmüştür. Bu açıdan, EEG nin doğru akım bileşeninin, beyin karmaşık açık sisteminin bir düzen parametresi olduğu hipotezi ortaya atılmıştır. Bu alandaki bilimsel çalışmaların, beynin bilişsel işlevleri ve otonom sinir sistemi arasındaki etkileşimlere yoğunlaştırılması önerilmiştir. Anahtar Sözcükler: EEG-biyogeribildirim, epilepsi, nöroterapi, yavaş kortikal gerilimler, nesnel tanı, tedavi değerlendirilmesi, nöroprofil, veri tabanı, epileptik örüntü nicelendirilmesi, fraktal boyut, bağıl negatif değişim, hiperventilasyon, doğru akım kayması, anlık nabız, sinerjetik.
7 Contents Acknowledgment... i Abstract...iii Zusammenfassung (Abstract in German)... iv Özet (Abstract in Turkish)...v Contents... vi List of Figures... x List of Tables...xiv List of Abbreviations...xv List of Symbols... xviii 1 Introduction Fundamentals: Epilepsy and EEG-Biofeedback Epilepsy Clinical Features Focal (Partial) and Generalized Epilepsy The Neurophysiology Therapies in Epilepsy Pharmacological Therapy Surgical Therapy Alternative Therapies Behavioral Approaches Neurotherapy in Epilepsy Sensorimotor Rhythm Studies Slow Cortical Potentials Studies DC-Potentials in the Brain...15
8 Contents vii Cortical DC-Shifts and Seizure Activity Cortical DC-Shifts and Gas Pressures in Blood and Tissue Cortical DC-Shifts and Cognitive Information Processing Cortical DC-Shifts and the Sleep-Wake Cycle Cortical DC-Shifts Associated with Anesthesia and Related Burst Suppression Problem Analysis: Objective (Neuro)therapy Planning and Evaluation in Epilepsy EEG in Epilepsy Quantitative EEG in Epilepsy Objectives Therapy Evaluation and the Neuroprofile The Therapy Evaluation Strategy The Neuroprofile Protocol for Evaluation Measurements Data Acquisition The EEG-Biofeedback System Evaluation Measurements EEG-Biofeedback Sessions Studies with Control Subjects Studies with Epilepsy Patients Signal Processing for Feature Extraction and Quantification Epileptic Pattern (Graphoelement) Analysis Measures of Pattern Characterization in EEG Värri Measures Fractal Dimension Supervised Quantification of Epileptic Patterns in EEG Unsupervised Quantification of Epileptic Patterns in EEG Adaptive EEG Segmentation Algorithm... 54
9 Contents viii Clustering the Obtained Segments Contingent Negative Variation Analysis Analysis of Hyperventilation Induced DC-Shifts Analysis of Hyperventilation Induced Changes in Instantaneous Heart Rate Software-Technical Aspects as a Basis for Automation The Neuroprofile Extraction Module Database Development Methodological Concerns Database Planning Requirements Analysis Data Warehouse Outline Software Concept Design and Implementation Conceptual Design Logical Design Physical Design Realization Results Epileptic Patterns Analysis Värri Measures versus Fractal Dimension Supervised and Unsupervised Detections Differences in the Contingent Negative Variation between Patients and Controls Differences in the Hyperventilation Induced DC-Shifts Instantaneous Heart Rate and Hyperventilation DC-Shifts and Instantaneous Heart Rate in Patients and Controls EEG-Biofeedback Adjustment and Learning...90
10 Contents ix 8.7 Application of the Methodology on Sample Cases for Pre- and Post-Therapy Comparisons Case I P2WM Case II P6RB Discussion Clinical Tools for Decision Supporting in (Neuro)therapy Evaluation Refinement of the Data Model Integration of Existing Clinical Databases Forming a Normative Database, Data Mining and Decision Supporting Selected Quantitative Measures Graphoelements Contingent Negative Variation Hyperventilation Induced DC-Shifts in EEG and Changes in IHR Quantification of Learning Understanding Neurotherapy Changing the Paradigm Self-Regulation and Self-Organization EEG-Biofeedback and Non-Linear Phase Transitions Concepts of Synergetics Neurotherapy as a Process of Learning: Operant Conditioning and Coordination Is the DC-level an Order Parameter of the Brain Complex Open System? Neurotherapy: An Interface of Psychology and Neurology via Synergetics? Summary and Conclusion References Appendix A Appendix B
11 List of Figures Fig. 2.1 EEG and DC/EEG. Principles of wave generation. The excitatory synapses of two afferent fibers contact the superficial dendritic arborisation of two longitudinal neuronal elements. The afferent fiber activity is recorded by means of the intracellular electrodes E1 and E2, and the membrane potentials (MP) of the dendritic elements are recorded by the electrodes E3 and E4. The field potential at the surface of the neuronal structure (cortex) is led by the electrode E5. Synchronized groups of action potentials in the afferent fibers (E1, E2) generate wavelike excitatory postsynaptic potentials (EPSPs) in the dendritic areas (E3, E4) and corresponding field potentials in the EEG and DC/EEG recording (E5). Tonic activity in the afferent fibers results in a long-lasting EPSP with small fluctuations. During this period the EEG (5b) shows only a reduction in amplitude, whereas the DC/EEG recording (5a) reflects the depolarization of the neuronal elements as well. [Erwin-Josef Speckmann and Christian E. Elger, Introduction to the Neurophysiological Basis of the EEG and DC Potentials in Ernst Niedermeyer and Fernando Lopes da Silva (Eds.), Electroencephalography: Basic principles, Clinical Applications, and Related Fields, p. 20, 4th Ed., Williams & Wilkins, Baltimore 1999.] Fig. 3.1 General therapy evaluation flow diagram Fig. 3.2 Components of the problem analysis and orientation Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 5.1 Fig. 5.2 Fig. 5.3 Sample section from the qeeg part of the neuroprofile model. The highlighted column shows the parameters extracted from EEG (e.g., DC-level; delta, theta, alpha1, alpha2-frequency band powers; amplitude of mu rhythm) from a single time-interval of a single provocation for a given channel. IEEE Transactions on Information Technology in Biomedicine Therapy evaluation flow diagram. Initial measurements, as well as evaluation measurements are analyzed by the data analysis module, which extracts the neuroprofile. Based on the neuroprofiles, therapy is evaluated and accordingly, is either continued unchanged or modified, or terminated Odd-ball paradigm. Ss = standard tone of 1000 Hz (duration 100 ms); St = target tone of 2000 Hz (duration 100 ms, occurrence 20%); t = 2 sec Modified S1-S2 paradigm. S1 = acoustic warning stimulus, S2 = visual aversive or non aversive stimulus. t1 = 6 sec, t2 = 6 sec, and t3 = 4 sec Paradigm for SMR measurements. Sl = visual stimulus for left hand thumb response Rl, Sr = visual stimulus for right hand thumb response Rr, So = visual stimulus for no-reaction. t = 3 sec Simplified block diagram of the developed EEG-biofeedback system. EEG/DC signals are acquired by the signal acquisition module, which is controlled and monitored by a separate software module. The signals are processed on-line, and the multimedia feedback is controlled by the extracted feedback parameter Modalities of the system. (a) Evaluation measurement using LCD glasses (28 channels EEG), (b) Biofeedback session (single channel EEG). The NeuroCybernetics Research Group Samples of the multimedia feedback components. (a) Airplane: amplitude controlled feedback, (b) Puzzle: band power controlled feedback. The NeuroCybernetics Research Group Fig. 5.4 The 28 channels of EEG acquired in an evaluation measurement (10/20 System) Fig. 5.5 An interval from the polygraphic signals acquired during an evaluation measurement
12 List of Figures xi Fig. 5.6 Acquired signals in different measurements from controls and patients Fig. 6.1 Block diagram of the analysis process Fig. 6.2 Supervised and non-supervised strategies for quantification of epileptic patterns. After pre-processing, measures need to be assigned for pattern characterization in both approaches. In the supervised path, the supervisor selects the pattern of interest from the real data, and subsequently similar patterns are searched for in the data. In the unsupervised path, EEG is segmented adaptively and the segments obtained are clustered by a classification algorithm Fig. 6.3 Signal processing steps for quantification of CNV Fig. 6.4 Signal processing steps for quantification of hyperventilation induced DC-shifts Fig. 6.5 Signal processing steps for IHR calculation during hyperventilation Fig. 7.1 Software components and flow diagram of the neuroprofile extraction module. Polygraphic data of different formats are read, interactively presented, and pre-processed for the main analysis, which contains software components for extraction of the relevant parameters. The quantitative results are stored in proprietary files. IEEE Transactions on Information Technology in Biomedicine Fig. 7.2 Graphic User Interface of the Neuroprofile Extraction Module Fig. 7.3 The database design process according to [92]. IEEE Transactions on Information Technology in Biomedicine Fig. 7.4 Datawarehouse outline. IEEE Transactions on Information Technology in Biomedicine Fig. 7.5 Fig. 7.6 Fig. 7.7 Fig. 8.1 Fig. 8.2 Fig. 8.3 Software concept using the database. 1 st development stage (current): Quantitative results are extracted both by the implemented sub-modules of the neuroprofile extraction module (version I) and the commercial software packages. They are entered into the data warehouse via a wrapper. Database frontend is used for maintenance of supplementary medical information. Results stored in the data warehouse can be visualized or statistically analyzed by the corresponding modules which have access via embedded SQL or ODBC. 2 nd development stage (future): Neuroprofile extraction module (Version II) shall overtake the quantitative analysis completely and store the results directly into the data warehouse. IEEE Transactions on Information Technology in Biomedicine A sample part from the entity-relationship diagram. Person, proband and epilepsy patient are central entity types of the model. Neurofeedback represents a session of neurofeedback training performed with a person, diagnostic raw data is a set of EEG data recorded for a particular person. IEEE Transactions on Information Technology in Biomedicine NeuroBase for entering clinical information. IEEE Transactions on Information Technology in Biomedicine Segmentation results for sample pattern 1, (a) Värri measures (threshold = 0.4 (*) and 0.45 (o)), (b) FD (threshold = 0.4). Window width = 1.1 sec, window overlapping = 60%. IEEE Engineering in Medicine and Biology Segmentation results for sample pattern 2, (a) Värri measures (threshold = 0.3 (*) and 0.4 (o)), (b) FD (threshold = 0.35). Window width = 1.1 sec, window overlapping = 60%. IEEE Engineering in Medicine and Biology Segmentation results for sample pattern 3, (a) Värri measures (threshold = 0.6 (*) and 0.75 (o)), (b) FD (threshold = 0.5). Window width = 1.1 sec, window overlapping = 60%. IEEE Engineering in Medicine and Biology... 77
13 List of Figures xii Fig. 8.4 Sample results of fuzzy clustering after adaptive segmentation based on FD on an EEG channel. Statistics (percentage occurrence) of the clusters which have the corresponding FD value as the center Fig. 8.5 Sample CNV results for 28 EEG channels. Initial measurement of subject S1JN. Time average of 20 sweeps Fig. 8.6 Quantification of CNV results, dcnv. Subject S5PT, initial measurement, channel Cz Fig. 8.7 Fig. 8.8 Topological mapping of the measure dcnv from, a) control subject S2MN, initial measurement; b) epilepsy patient P4ES, pre-therapy measurement DC-level during a standard measurement for all EEG electrodes. Subject S5PT, initial measurement. Triggers: F6=HV-start, F7=HV-end, F4=recovery-end Fig. 8.9 DC-shifts during and after hyperventilation at electrode positions Fp1, Fp2, Fz, Cz, Pz, Oz, O1, and O2. t = 0, hyperventilation starts; t = 185 s, hyperventilation ends. Subject S5PT, 1 st evaluation measurement Fig DC-shifts during and after hyperventilation at electrode positions Fp1, Fp2, Fz, Cz, Pz, Oz, O1, O2. t = 0, hyperventilation starts; t = 176 s, hyperventilation ends. Patient P2WM, pre-therapy measurement Fig Linear regression for determining the rate of change of DC-level within HV and recovery intervals Fig Topological mapping of the rate of change of DC-level for a control subject (S1JN, initial measurement), a) hyperventilation (shv), and b) recovery (srec) Fig Topological mapping of the rate of change of DC-level for an epilepsy patient (P2WM, pre-therapy measurement), a) hyperventilation (shv), and b) recovery (srec) Fig IHR analysis result during HV and recovery for a control subject (S4OL). (a) the ECG channel after pre-processing from the standard I measurement, (b) the detected R peaks (an interval zoomed from (a)), (c) the corresponding IHRC Fig IHR analysis result during HV and recovery for a patient (P2WM). (a) the ECG channel after pre-processing from the standard I measurement, (b) the detected R peaks (an interval zoomed from (a)), (c) the corresponding IHRC Fig srec at the vertex (Cz) versus %HRrec/hv in patients (PT) and controls (CS) Fig DCIrec at the vertex versus HRIhv in patients and controls Fig DC-shifts during and after hyperventilation at electrode positions Fp1, Fp2, Fz, Cz, Pz, Oz, O1, and O2. t = 0, hyperventilation starts; t = 176 s, hyperventilation ends. Patient P2WM, evaluation measurement Fig CNV results of evaluation measurements in patient P2WM, (a) evaluation measurement 1 (pre-therapy), (b) evaluation measurement 2, (c) evaluation measurement Fig CNV results of evaluation measurements in patient P5RB, (a) evaluation measurement 1 (pre-therapy), (b) evaluation measurement 2, (c) evaluation measurement Fig. 9.1 Results of an EEG-biofeedback session based on SCP (black: negativation task, red: positivation task), (a) feedback channel Fcz, (b) respiration channel ATHM, and corresponding spectral power of the respiration channel (c) negativation task, (b) positivation task. RGESIM/ASIM Verlag-Proceedings of World Congress on Neuroinformatics
14 List of Figures xiii Fig. 9.2 Synergetical representation of microscopic and macroscopic interactions, and corresponding parameters. Psychology as a higher macroscopic level is excluded for simplification
15 List of Tables Table 4-1 The protocol for evaluation meaurements Table 5-1 Measurements carried out with control subjects Table 5-2 Measurements carried out with epilepsy patients Table 8-1 CNV Comparison between patients and control subjects. IEEE Transactions on Information Technology in Biomedicine Table 8-2 Rate of change of DC-level within HV and recovery intervals for control subjects Table 8-3 Rate of change of DC-level within HV and recovery intervals for patients Table 8-4 Percentage DC-recovery after hyperventilation in patients and controls Table 8-5 Measures HRbsl, HRhv, HRrec and the indices %HRrec/hv and %HRIhv for control subjects in initial measurements Table 8-6 Measures HRbsl, HRhv, HRrec and the indices %HRrec/hv and %HRIhv for patients in pre-therapy measurements Table 8-7 Results of follow-up for patient P2WM Table 8-8 Results of follow-up for patient P5RB... 94
16 List of Abbreviations ADD ADHD ATHM BCI BP CEN CNS CNV CSE CT DBMS DC EBS ECG EDF EEG EOG ERD ERM ERP FCMI FD FFT fmri Attention Deficit Disorder Attention Deficit Hyperactivity Disorder Respiration curve Brain-computer-interface Bereitschaftspotential European Standardization Committee Central Nervous System Contingent Negative Variation Common Standards for Quantitative Electrocardiography Computed Tomography Database management system Direct current Extensible Biosignal Format Electrocardiogram European Data Format Electroencephalogram Electrooculogram Entity-relationship diagram Entity-relationship modeling Event-related Potentials Fuzzy c-means iterative algorithm Fractal dimension Fast Fourier Transformation Functional Magnetic Resonance imaging
17 List of Abbreviations xvi GABA GUI HEOG HV IAPS ID IHR IHRC ILAE LTP MEG NCRG NMDA ODBC OEDIPE PDS PET PS qeeg SCP SMR SNOMED SP SPECT SQL Gamma-amino butyric acid Graphical user interface Horizontal electrooculogram Hyperventilation International Affective Picture System Identity code Instantaneous heart rate Instantaneous heart rate curve International League Against Epilepsy Long-term potentiation Magnetoencephalogram NeuroCybernetics Research Group N-methyl-D-aspartate Open database connectivity Open European Data Interchange and Processing for Electrocardiography Paroxysmal depolarization shift Positron Emission Tomography Photostimulation Quantitative electroencephalogram Slow Cortical Potentials Sensorimotor rhythm Systematized Nomenclature of Medicine Slow potentials Single Photon Emission Computed Tomography Structured Query Language TC251 Technical Committee 251 VEOG Vertical electrooculogram
18 List of Abbreviations xvii VITAL VM Vital Signs Information Representation Värri measures
19 List of Symbols a c a p, b p, c p A ADIF B c c a c f Ca + d CNV D DCI rec E EPO f F FDIF FD G GV k GD k Hz Adaptation constant Measures of a pattern Amplitude measure Amplitude difference measure Block size of analysis in data points Number of clusters Amplitude difference measure coefficient Frequency difference measure coefficient Calcium ion Contingent negative variation level difference measure Euclidean distance Direct current recovery index Maximum error Epileptic pattern occurrence measure Number of measures used for pattern characterization Derivate measure Frequency difference measure Fractal dimension Measure difference function Measure difference function of Värri measures Measure difference function of fractal dimension Hertz
20 List of Symbols xix HR bsl HR hv HR rec HR rec/hv HRI hv I f (i) K + L lt m ml min ms mv M Na + ol p m P ij pco 2 po 2 ph Q n R R l R r s Baseline heart rate Average heart rate during hyperventilation Average heart rate during recovery Heart rate recovery ratio Hyperventilation heart rate index Local fuzzy performance index Potassium ion Curve length Liters Number of patterns Milliliters Minutes Milliseconds Millivolts Maximum number of iterations Sodium ion Overlap of successive windows in data points EEG segments Degree of membership of pattern p j Partial pressure of carbon dioxide Partial pressure of oxygen Acidity Adaptive recursive threshold function Estimated diameter Left hand thumb response in SMR paradigm Right hand thumb response in SMR paradigm Seconds
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text:
Injured Oklahoma quarterback Sam Bradford made a visit to Dr. James Andrews in an attempt to see whether he could be cleared in time for the Sooners' game next Saturday against Miami (Fla.).
Bradford has been consulting with Andrews, a renowned sports surgeon, since hurting his shoulder against Brigham Young in the opener on Sept. 5. He was expected to miss 2-4 weeks.
WAPT, a television station in Jackson, Miss., reported that Bradford boarded a plane to Birmingham, Ala., at Love Field late Friday night, on his way to Andrews' office.
Christian Steckel, the sports director for WAPT, said the visit does not mean that surgery is imminent.
"There is no talk of surgery that we know of on the record, this is further evaluation on the original injury," said Steckel, who broke the story on his ESPN radio show before running a story on-air. "He wants to return to play next week (against Miami) and he's been in consultation with Dr. Andrews from the beginning, but he's going to make an appearance in front of him on this so he can make a better evaluation."
Steckel said his information came from sources within Bradford's inner circle.
In Bradford's place, freshman quarterback Landry Jones has led Oklahoma to 109 points in two wins, including passing for a school-record six touchdown passes against Tulsa.
Hat tip: The Daily Oklahoman
First-place votes in parentheses.
Football coaches' poll
Sign up for Campus Rivalry e-mail alerts

subset: commoncrawl


text:
If there is one thing that parents would like their children to learn, it's to seek lifelong good health. The best way to get the ball rolling is to serve as a role model. Teach your children the important habits of good health, like preventive eye exams.
When should children get their first eye examination?
A routine eye examination is a normal part of the physical given to infants and children by their neonatologist or pediatrician. Your baby will be examined at birth for any congenital problems. Though these problems are rare, diagnosis is crucial for your baby's proper sight and eye development. At each well child exam, the pediatrician will check for problems. If you or the child's doctor have any concerns, the child should be referred for evaluation. All children should be screened for amblyopia prior to their fifth birthday. Amblyopia is a condition in which one eye sees poorly and is often successfully treated using glasses, patching, drops, or surgery depending on the cause of the problem.
How should I prepare my child for an eye exam?
Make time to sit down and explain what will happen during the eye exam. Make sure your child knows that he will be asked to look at and identify objects for the practitioner. These could be random pictures, letters, or spots of light on the wall. Explain also that the doctor may put drops in his eyes that will help to see inside of his eyes better, but it will not hurt.
What tests will be done on my child?
At the age of six months, the optometrist will check for nearsightedness, farsightedness, astigmatism, eye movement ability, proper eye alignment reaction to changes in light and darkness (red papillary light reflex), and any general eye health problems. An irregular light reflex may be a sign of abnormalities within the structure of the eye. These could include cataracts (clouding of the lens), how well the eye focuses light, or possibly tumors. At any time, if problems are found during the examination, the child will usually be referred to a pediatric ophthalmologist, who specializes in diagnosing and treating eye conditions in children. Early diagnosis of childhood eye disease is crucial to effective treatment.
For children between the ages of 3 and 5 years, the eye doctor will conduct a physical examination of the eyes. The doctor will also conduct vision screenings using eye chart tests, pictures, letters, or the "tumbling E game" to test the child's visual acuity, or ability to see form and detail of objects. The "tumbling E game," also called the "Random E's Visual Acuity Test," is useful in determining visual acuity for children who cannot yet read. The child is asked to identify the direction that the letter "E" opens to by holding out 4 fingers to mimic the letter "E." Correcting poor visual acuity is very important in a child's sight development.
Helping your child adjust to wearing glasses
A successful visit to the eye doctor is only half the battle of improving your child's sight. Convincing your child to wear his or her prescription glasses is also important. Here are some suggestions for helping your child with this adjustment.
- Your child's frames should fit properly, without pinching the ears or nose, or weighing down the face. Check points of contact periodically to make sure that there is no skin irritation.
- Make sure your child is wearing the correct prescription, especially if wearing glasses is uncomfortable. An optician or eye doctor will be able to determine the optical accuracy of the prescription.
- Start your child wearing glasses for short periods of time and gradually increase the time.
- Make the glasses part of the child's daily routine--putting them on in the morning when dressing and taking them off at night before going to bed.
- Be sure to use positive reinforcement when the child does wear the glasses.
Signs of poor vision: When does a child need further evaluation?
How will you know that your child needs a more detailed examination or specialized ophthalmologic care? Pediatricians, family doctors, school nurses, and vision-screening personnel have used a variety of criteria for determining which children require more comprehensive eye evaluations by an ophthalmologist. The American Academy of Pediatrics Section on Ophthalmology, in cooperation with the American Association for Pediatric Ophthalmology and Strabismus and the American Academy of Ophthalmology, have developed guidelines for use by all pediatric vision screening professionals:
- Children are referred to the ophthalmologist if they have a visual acuity of less than 20/40 or if there are two or more lines of difference in acuity between both eyes. Children 6 years of age and older are referred if vision is 20/30 or less or if there is a two-line difference between the eyes.
- Children with a family history of significant childhood eye problems should be examined early in life for the presence of similar problems. Your pediatrician will complete a vision screening evaluation, but a detailed examination is best performed by the pediatric ophthalmologist.
© Copyright 1995-2017 The Cleveland Clinic Foundation. All rights reserved.
This information is provided by the Cleveland Clinic and is not intended to replace the medical advice of your doctor or health care provider. Please consult your health care provider for advice about a specific medical condition. This document was last reviewed on: 2/10/2010...#8571


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Summit County moose killing sparks Colorado Parks and Wildlife probe
December 13, 2013
Colorado Parks and Wildlife officers are seeking information about the death of a bull moose found last week near Frey Gulch Road on Tenderfoot Mountain, east of the Summit County public shooting range.
The moose reportedly died of a gunshot wound and was not field dressed, leaving the meat to waste, according to a Parks and Wildlife news release.
The moose was discovered during Colorado's second rifle hunting season, but wildlife officers think the animal was killed during the first rifle season, which took place Oct. 12 through 16. Although the circumstances are currently unknown, officials are investigating the incident as a possible mistaken or careless kill by an elk hunter, the release stated.
Mike Porras, public information officer for Colorado Parks and Wildlife, said Monday accidental or careless kills are few, but still too frequent in Colorado. Wildlife officers investigate several cases each year. However, more often than not hunters take responsibility for their actions by alerting the authorities and by properly field dressing the animal to ensure the meat doesn't go to waste.
Although the suspect responsible has not yet stepped forward, Porras said the hunter still has an opportunity to claim responsibility. Wildlife officers are granted a lot of discretion in how they pursue charges for accidental or careless kills, Porras said, but the clock is ticking.
"We know accidents can happen and we also know there are sometimes cases in which the hunter acted carelessly, but the person that did this still has the opportunity to do the right thing and turn themselves in immediately," Porras said. "What we're saying here is if you let our officers know, they're going to take that into consideration, but if you evade authorities, significant penalties are going to apply."
According to Colorado Revised Statutes, a hunter found guilty of poaching or careless hunting could face felony charges that may result in steep fines, jail time and the suspension of their hunting privileges.
The revocation of hunting privileges would not only be enforced in Colorado, but also among the 38 partner states of the Interstate Wildlife Violator Compact. Hunters who accrue 20 points against their license over a five-year period could have their hunting privileges suspended indefinitely, according to Colorado law.
In this case, Porras said it would not be unlikely for the hunter responsible to face waste of meat charges, which is a misdemeanor in Colorado and carries a fine of $300 and a penalty of 15 hunting license suspension points. Hunting in a careless manner, also a misdemeanor offense, is punishable by a fine of $100 to $1,000 and up to one year in the county jail.
Although several other charges could be levied against the individual if the hunter is brought to justice, the most serious could come under Colorado's Samson Law. Charges under Samson are typically levied in cases in which a trophy animal is taken illegally. Additional fines range from $4,000 for a trophy antelope to $25,000 for a trophy big horn sheep. The illegal killing of any bull moose carries a fine of $10,000.
Colorado Parks and Wildlife urges the public to provide any additional information that may lead to the person or persons responsible, including personal photos of any live bull moose seen in the area since early October.
"There may be people out there who witnessed something they think is insignificant, but even the smallest detail could be that link that helps us solve the larger puzzle," Porras said. "If anyone has any information, they are encouraged to contact us immediately."
Persons with information can contact Operation Game Thief, a wildlife tips hotline at 877-265-6648. Callers will remain anonymous and cash rewards may be given if the information leads to a conviction, the release stated.
For more information about Operation Game Thief, visit http://www.wildlife.state.co.us/RulesRegs/LawEnforcement/OperationGameThief/Pages/OGT.aspx.
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text:
Hi, so i'm tring to instal niresh 10.8.5 on my gygabyte MB with amd fx CPU
i tried both iso and usb
When i put my sata in AHCI mode, i'm not able to boot with the disc
Only when i put my sata in IDE i'm able to start from the disc and launch the installation with AMD64 bootflag.. but installation chrash at the end, grey screen.
I tried to boot with the usb version in AHCI mode, but i don't know how to add bootflag with the usb version so i'm stuck with the apple screen at the beginning of the installation..
Any help would be apreciated
- 0
Niresh 10.8.5 AHCI + instal crash
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Hi, so i'm tring to instal niresh 10.8.5 on my gygabyte MB with amd fx CPU
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