Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

This study’s aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.Facultad de Ingenierí

Using the Information Provided by Forbidden Ordinal Patterns in Permutation Entropy to Reinforce Time Series Discrimination Capabilities

[EN] Despite its widely tested and proven usefulness, there is still room for improvement in the basic permutation entropy (PE) algorithm, as several subsequent studies have demonstrated in recent years. Some of these new methods try to address the well-known PE weaknesses, such as its focus only on ordinal and not on amplitude information, and the possible detrimental impact of equal values found in subsequences. Other new methods address less specific weaknesses, such as the PE results¿ dependence on input parameter values, a common problem found in many entropy calculation methods. The lack of discriminating power among classes in some cases is also a generic problem when entropy measures are used for data series classification. This last problem is the one specifically addressed in the present study. Toward that purpose, the classification performance of the standard PE method was first assessed by conducting several time series classification tests over a varied and diverse set of data. Then, this performance was reassessed using a new Shannon Entropy normalisation scheme proposed in this paper: divide the relative frequencies in PE by the number of different ordinal patterns actually found in the time series, instead of by the theoretically expected number. According to the classification accuracy obtained, this last approach exhibited a higher class discriminating power. It was capable of finding significant differences in six out of seven experimental datasets¿whereas the standard PE method only did in four¿and it also had better classification accuracy. It can be concluded that using the additional information provided by the number of forbidden/found patterns, it is possible to achieve a higher discriminating power than using the classical PE normalisation method. The resulting algorithm is also very similar to that of PE and very easy to implement.Cuesta Frau, D. (2020). Using the Information Provided by Forbidden Ordinal Patterns in Permutation Entropy to Reinforce Time Series Discrimination Capabilities. Entropy. 22(5):1-17. https://doi.org/10.3390/e22050494S117225Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17). doi:10.1103/physrevlett.88.174102Zanin, M., Zunino, L., Rosso, O. A., & Papo, D. (2012). Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review. Entropy, 14(8), 1553-1577. doi:10.3390/e14081553Li, J., Yan, J., Liu, X., & Ouyang, G. (2014). Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures. Entropy, 16(6), 3049-3061. doi:10.3390/e16063049Ravelo-García, A., Navarro-Mesa, J., Casanova-Blancas, U., Martin-Gonzalez, S., Quintana-Morales, P., Guerra-Moreno, I., … Hernández-Pérez, E. (2015). Application of the Permutation Entropy over the Heart Rate Variability for the Improvement of Electrocardiogram-based Sleep Breathing Pause Detection. Entropy, 17(3), 914-927. doi:10.3390/e17030914Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Jordán-Núñez, J., Vargas, B., González, P., & Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20(11), 853. doi:10.3390/e20110853Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., & Vigil, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine, 165, 197-204. doi:10.1016/j.cmpb.2018.08.018Gao, Y., Villecco, F., Li, M., & Song, W. (2017). Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis. 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Practical Influence on Permutation Entropy and Its Applications. Entropy, 21(4), 385. doi:10.3390/e21040385KumarSingh, B., Verma, K., & S. Thoke, A. (2015). Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification. International Journal of Computer Applications, 116(19), 11-15. doi:10.5120/20443-2793Talukder, B., W. Hipel, K., & W. vanLoon, G. (2017). Developing Composite Indicators for Agricultural Sustainability Assessment: Effect of Normalization and Aggregation Techniques. Resources, 6(4), 66. doi:10.3390/resources6040066Tofallis, C. (2014). Add or Multiply? A Tutorial on Ranking and Choosing with Multiple Criteria. INFORMS Transactions on Education, 14(3), 109-119. doi:10.1287/ited.2013.0124Henry, M., & Judge, G. (2019). Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series. Econometrics, 7(1), 10. doi:10.3390/econometrics7010010Zanin, M. (2008). Forbidden patterns in financial time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18(1), 013119. doi:10.1063/1.2841197Cuesta-Frau, D., Molina-Picó, A., Vargas, B., & González, P. (2019). Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy. Entropy, 21(10), 1013. doi:10.3390/e21101013Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6). doi:10.1103/physreve.64.061907Bellegdi, S. A., & Arafat, S. M. A. (2017). Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands. International Journal of Applied Mathematics, Electronics and Computers, 1(SpecialIssue), 36-41. doi:10.18100/ijamec.2017specialissue30468Hussain, L., Aziz, W., Alowibdi, J. S., Habib, N., Rafique, M., Saeed, S., & Kazmi, S. Z. H. (2017). Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states. Journal of Physiological Anthropology, 36(1). doi:10.1186/s40101-017-0136-8Cuesta–Frau, D., Miró–Martínez, P., Jordán Núñez, J., Oltra–Crespo, S., & Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine, 87, 141-151. doi:10.1016/j.compbiomed.2017.05.028Sharmila, A. (2018). Epilepsy detection from EEG signals: a review. Journal of Medical Engineering & Technology, 42(5), 368-380. doi:10.1080/03091902.2018.1513576Molina-Picó, A., Cuesta-Frau, D., Aboy, M., Crespo, C., Miró-Martínez, P., & Oltra-Crespo, S. (2011). Comparative study of approximate entropy and sample entropy robustness to spikes. 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Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series. Entropy, 20(3), 148. doi:10.3390/e20030148Iyengar, N., Peng, C. K., Morin, R., Goldberger, A. L., & Lipsitz, L. A. (1996). Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 271(4), R1078-R1084. doi:10.1152/ajpregu.1996.271.4.r1078Liu, C., Li, K., Zhao, L., Liu, F., Zheng, D., Liu, C., & Liu, S. (2013). Analysis of heart rate variability using fuzzy measure entropy. Computers in Biology and Medicine, 43(2), 100-108. doi:10.1016/j.compbiomed.2012.11.005Bugenhagen, S. M., Cowley, A. W., & Beard, D. A. (2010). Identifying physiological origins of baroreflex dysfunction in salt-sensitive hypertension in the Dahl SS rat. Physiological Genomics, 42(1), 23-41. doi:10.1152/physiolgenomics.00027.2010Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. 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Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition

Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; EspañaFil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; EspañaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; EspañaFil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; Españ

Ordinal Patterns in Heartbeat Time Series: An Approach Using Multiscale Analysis

In this paper, we simultaneously use two different scales in the analysis of ordinal patterns to measure the complexity of the dynamics of heartbeat time series. Renyi entropy and weighted Renyi entropy are the entropy-like measures proposed in the multiscale analysis in which, with the new scheme, four parameters are involved. First, the influence of the variation of the new parameters in the entropy values is analyzed when different groups of subjects (with cardiac diseases or healthy) are considered. Secondly, we exploit the introduction of multiscale analysis in order to detect differences between the groups.The author has been supported by the Grant MTM2017-84079-P from Agencia Estatal de Investigacion (AEI) y Fondo Europeo de Desarrollo Regional (FEDER). The author thanks anonymous reviewers for their helpful suggestions and comments that have contributed to improving and clarifying this manuscript

Mutual information between heart rate variability and respiration for emotion characterization

Objective: Interest in emotion recognition has increased in recent years as a useful tool for diagnosing psycho-neural illnesses. In this study, the auto-mutual and the cross-mutual information function, AMIF and CMIF respectively, are used for human emotion recognition. Approach: The AMIF technique was applied to heart rate variability (HRV) signals to study complex interdependencies, and the CMIF technique was considered to quantify the complex coupling between HRV and respiratory signals. Both algorithms were adapted to short-term RR time series. Traditional band pass filtering was applied to the RR series at low frequency (LF) and high frequency (HF) bands, and a respiration-based filter bandwidth was also investigated (). Both the AMIF and the CMIF algorithms were calculated with regard to different time scales as specific complexity measures. The ability of the parameters derived from the AMIF and the CMIF to discriminate emotions was evaluated on a database of video-induced emotion elicitation. Five elicited states i.e. relax (neutral), joy (positive valence), as well as fear, sadness and anger (negative valences) were considered. Main results: The results revealed that the AMIF applied to the RR time series filtered in the band was able to discriminate between the following: relax and joy and fear, joy and each negative valence conditions, and finally fear and sadness and anger, all with a statistical significance level p¿-value 0.05, sensitivity, specificity and accuracy higher than 70% and area under the receiver operating characteristic curve index AUC 0.70. Furthermore, the parameters derived from the AMIF and the CMIF allowed the low signal complexity presented during fear to be characterized in front of any of the studied elicited states. Significance: Based on these results, human emotion manifested in the HRV and respiratory signal responses could be characterized by means of the information-content complexityPeer ReviewedPostprint (author's final draft

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy

Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

Background: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded at https://bitbucket.org/deepak_panday/ceps/src/pipeline_v2/. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing

Characterization of the autonomic nervous system response under emotional stimuli through linear and non-linear analysis of physiological signals

En esta disertación se presentan metodologías lineales y no lineales aplicadas a señales fisiológicas, con el propósito de caracterizar la respuesta del sistema nervioso autónomo bajo estímulos emocionales. Este estudio está motivado por la necesidad de desarrollar una herramienta que identifique emociones en función de su efecto sobre la actividad cardíaca, ya que puede tener un impacto potencial en la práctica clínica para diagnosticar enfermedades psico-neuronales.Las hipótesis de esta tesis doctoral son que las emociones inducen cambios notables en el sistema nervioso autónomo y que estos cambios pueden capturarse a partir del análisis de señales fisiológicas, en particular, del análisis conjunto de la variabilidad del ritmo cardíaco (HRV) y la respiración.La base de datos analizada contiene el registro simultáneo del electrocardiograma y la respiración de 25 sujetos elicitados con emociones inducidas por vídeos, incluyendo las siguientes emociones: alegría, miedo, tristeza e ira.En esta disertación se describen dos estudios metodológicos.En el primer estudio se propone un método basado en el análisis lineal de la HRV guiado por la respiración. El método se basó en la redefinición de la banda de alta frecuencia (HF), no solo centrándose en la frecuencia respiratoria, sino también considerando un ancho de banda que dependiera del espectro respiratorio. Primero, el método se validó con señales de HRV simuladas, obteniéndose errores mínimos de estimación en comparación con la definición de la banda de HF clásica e incluso con la banda de HF centrada en la frecuencia respiratoria pero con un ancho de banda constante, independientemente de los valores del ratio simpático-vagal.Después, el método propuesto se aplicó en una base de datos de elicitación emocional inducida mediante vídeos para discriminar entre emociones. No solo la banda de HF redefinida propuesta superó a las otras definiciones de banda de HF en discriminación emocional, sino también la correlación máxima entre los espectros de la HRV y de la respiración discriminó alegría y relajación, alegría y cada emoción de valencia negativa y entre miedo y tristeza con un p-valor ≤ 0.05 y AUC ≥ 0.70.En el segundo estudio, técnicas no lineales como la Función de Auto Información Mutua y la Función de Información Mutua Cruzada, AMIF y CMIF respectivamente, son también propuestas en esta tesis doctoral para el reconocimiento de emociones humanas. La técnica AMIF se aplicó a las señales de HRV para estudiar interdependencias complejas, y se consideró la técnica CMIF para cuantificar el acoplamiento complejo entre las señales de HRV y de respiración. Ambos algoritmos se adaptaron a las series temporales RR de corta duración. Las series RR fueron filtradas en las bandas de baja y alta frecuencia, y también se investigaron las series RR filtradas en un ancho de banda basado en la respiración.Los resultados revelaron que la técnica AMIF aplicada a la serie temporal RR filtrada en la banda de HF redefinida fue capaz de discriminar entre: relajación y alegría y miedo, alegría y cada valencia negativa y finalmente miedo y tristeza e ira, todos con un nivel de significación estadística (p-valor ≤ 0.05, AUC ≥ 0.70). Además, los parámetros derivados de AMIF y CMIF permitieron caracterizar la baja complejidad que la señal presentaba durante el miedo frente a cualquier otro estado emocional estudiado.Finalmente se investiga, mediante un clasificador lineal, las características lineales y no lineales que discriminan entre pares de emociones y entre valencias emocionales para determinar qué parámetros permiten diferenciar los grupos y cuántos de éstos son necesarios para lograr la mejor clasificación posible. Los resultados extraídos de este capítulo sugieren que pueden ser clasificadas mediante el análisis de la HRV: relajación y alegría, la valencia positiva frente a todas las negativas, alegría y miedo, alegría y tristeza, alegría e ira, y miedo y tristeza.El análisis conjunto de la HRV y la respiración aumenta la capacidad discriminatoria de la HRV, siendo la máxima correlación entre los espectros de la HRV y la respiración uno de los mejores índices para la discriminación de emociones. El análisis de la información mutua, aun en señales de corta duración, añade información relevante a los índices lineales para la discriminación de emociones.<br /

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Математичний аналіз варіабельності ритму серця під час сну для визначення стану організму

Актуальність теми. Дослідження показують що пацієнтів із тривалою епілепсією багато судом пов'язано зі зміною тонусу вегетативної нервової системи (ВНС). Тонус ВНС можна оцінити за допомогою параметрів варіабельності серцевого ритму (ВСР). Кілька досліджень свідчать про те, що зміни тонусу ВНС, як правило, передують початку електроенцефалографічних змін (ЕЕГ), пов’язаних з епілептичними нападами, припускаючи, що зміни тонусу ВНС можуть бути використані в системах сигналізації та втручання. Розпізнавання цих коротко- та довгострокових серцевих ефектів на основі аналізу ВСР нелінійними методами стане в нагоді при прогнозуванні судом і допоможе в індивідуальному лікуванні хворих. Метою дослідження є виявлення відмінностей у нелінійних параметрах ВСР до та після епілептичного нападу. Для досягнення поставленої мети потрібно виконати такі задачі дослідження: - розглянути засоби реєстрація та вимірювання ВСР - дослідити методи аналізу ВСР - дослідити фізіологічні прояви ВСР - провести програмний розрахунок нелінійних параметрів ВСР за різний час до та після епілептичного нападу - статистично оцінити зміну нелінійних параметрів після епілептичного нападу. Об’єктом дослідження є RR-інтервали до та після епілептичних нападів. 5 Предметом дослідження є застосування методів аналізу варіабельності ритму серця для отримання параметрів до- та після епілептичного нападу. Методи дослідження: Приблизна ентропія (ApEn), Ентропія вибірки (SampEn), Ентропія перестановки (PE), Ентропія сингулярного розкладу (SvdEn), Спектральна ентропія (SE), Показник Херста (HE), Кореляційна розмірність (CD), Аналіз детермінованих коливань (DFA). Практичне значення одержаних результатів полягає можливості створення систем для детектування епілептичних нападів з довготривалих записів серцевих скорочень та прогнозування нападів у хворих під час сну.Actuality of theme. Studies show that many patients with long-term epilepsy are associated with changes in the tone of the autonomic nervous system (ANS). VNS tone can be assessed using heart rate variability (HRV) parameters. Several studies suggest that changes in ANS tone usually precede the onset of electroencephalographic changes (EEG) associated with epileptic seizures, suggesting that changes in ANS tone can be used in signaling and intervention systems. Recognition of these short- and long-term cardiac effects based on HRV analysis by nonlinear methods will be useful in predicting seizures and will help in the individual treatment of patients. The aim of the study is to identify differences in the nonlinear parameters of HRV before and after an epileptic seizure. To achieve this goal you need to perform the following research tasks: - consider the means of registration and measurement of HRV - to investigate the methods of HRV analysis - to investigate the physiological manifestations of HRV - to perform a program calculation of nonlinear HRV parameters at different times before and after an epileptic seizure - statistically evaluate the change in nonlinear parameters after an epileptic seizure. The object of the study is the RR intervals before and after epileptic seizures. The subject of the study is the use of methods for analyzing heart rate variability to obtain parameters before and after an epileptic seizure. 7 Research methods: Approximate entropy (ApEn), Sampling entropy (SampEn), Permutation entropy (PE), Singular decomposition entropy (SvdEn), Spectral entropy (SE), Hearst index (HE), Correlation dimension (CD), Deterministic oscillation analysis (DFA). The practical significance of the results is the possibility of creating systems for detecting epileptic seizures from long-term recordings of heart contractions and predicting seizures in patients during sleep