Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge

[EN] The effects of sleep-related disorders, such as obstructive sleep apnea (OSA), can be devastating either in children or adults. Misdiagnosis may lead to severe cardiovascular diseases. Besides, OSA consequences are often related to bad job performance, and road accidents. Nowadays, polysomnography (PSG) is still considered the gold standard for OSA diagnosis, but the required facilities are extremely high, thus reducing availability worldwide. For this reason, simpler and cost-effective diagnosing methods have been proposed in the late years. In this regard, the heart rate variability (HRV) has been demonstrated to strongly reflect apnea episodes during sleep. Hence, this work reviews the latest advances in the evaluation of OSA from the HRV perspective to consider its potentialities for a future revisited CinC Challenge.This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Polit ' ecnica de Cuenca and Instituto de Tecnolog ' ias Audiovisuales, University of CastillaLa ManchaPadovano, D.; Martinez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2020). Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.400S1

Application of the entropy of approximation for the nonlinear characterizationin patients with Chagas disease

Chagas disease American trypanosomiasis is caused by a flagellated parasite: Trypanosoma cruzi, transmitted by an insect of the genus Triatoma and also by blood transfusions. In Latin America, the number of infected people is approximately 6 million, with a population exposed to the risk of infection of 550000. It is our interest to develop a non-invasive and low-cost methodology, capable of detecting any early cardiac alteration that also allows us to see dysautonomia or dysfunction within 24 hours and with this it could be used to detect any cardiac alteration caused by T early Cruzi. For this, we analyzed the 24-hour Holter ECG records in 107 patients with ECG abnormalities (CH2), 102 patients without ECG alterations (CH1) who had positive serological results for Chagas disease and 83 volunteers without positive serological results for Chagas disease (CONTROL). Approximate entropy was used to quantify the regularity of electrocardiograms (ECG) in the three groups. We analyzed 288 ECG segments per patient. Significant differences were found between the CONTROL-CH1, CONTROL-CH2 and CH1-CH2 groups.Postprint (published version

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, k-fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.This research has received financial support from public grants PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund, SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Moreover, Daniele Padovano holds a predoctoral scholarship 2022-PRED-20642, which is cofinanced by the operating program of European Social Fund (ESF) 2014-2020 of Castilla-La Mancha.Padovano, D.; Martínez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2022). On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning. IEEE Access. 10:92710-92725. https://doi.org/10.1109/ACCESS.2022.320191192710927251

Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations

[EN] Despite its widely demonstrated usefulness, there is still room for improvement in the basic Permutation Entropy (PE) algorithm, as several subsequent studies have proposed in the recent years. For example, some improved PE variants try to address possible PE weaknesses, such as its only focus on ordinal information, and not on amplitude, or the possible detrimental impact of equal values in subsequences due to motif ambiguity. Other evolved PE methods try to reduce the influence of input parameters. A good representative of this last point is the Bubble Entropy (BE) method. BE is based on sorting relations instead of ordinal patterns, and its promising capabilities have not been extensively assessed yet. The objective of the present study was to comparatively assess the classification performance of this new method, and study and exploit the possible synergies between PE and BE. The claimed superior performance of BE over PE was first evaluated by conducting a series of time series classification tests over a varied and diverse experimental set. The results of this assessment apparently suggested that there is a complementary relationship between PE and BE, instead of a superior/inferior relationship. A second set of experiments using PE and BE simultaneously as the input features of a clustering algorithm, demonstrated that with a proper algorithm configuration, classification accuracy and robustness can benefit from both measures.Cuesta Frau, D.; Vargas-Rojo, B. (2020). Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations. Mathematical Biosciences and Engineering. 17(2):1637-1658. https://doi.org/10.3934/mbe.2020086S163716581721. C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett., 88 (2002), 174102.2. M. Zanin, L. Zunino, O. A. Rosso and D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review, Entropy, 14 (2012), 1553-1577.14. F. Siokis, Credit market jitters in the course of the financial crisis: A permutation entropy approach in measuring informational efficiency in financial assets, Phys. A Statist. Mechan. Appl., 499 (2018).15. A. F. Bariviera, L. Zunino, M. B. Guercio, L. Martinez and O. Rosso, Efficiency and credit ratings: A permutation-information-theory analysis, J. Statist. Mechan. Theory Exper., 2013 (2013), P08007.16. A. F. Bariviera, M. B. Guercio, L. Martinez and O. Rosso, A permutation information theory tour through different interest rate maturities: the libor case, Philos. Transact. Royal Soc. A Math. Phys. Eng. Sci., 373 (2015).20. B. Fadlallah, B. Chen, A. Keil and J. Príncipe, Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information, Phys. Rev. E, 87 (2013), 022911.Deng, B., Cai, L., Li, S., Wang, R., Yu, H., Chen, Y., & Wang, J. (2016). Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease. Cognitive Neurodynamics, 11(3), 217-231. doi:10.1007/s11571-016-9418-924. D. Cuesta-Frau, Permutation entropy: Influence of amplitude information on time series classification performance, Math. Biosci. Eng., 5 (2019), 1-16.25. F. Traversaro, M. Risk, O. Rosso and F. Redelico, An empirical evaluation of alternative methods of estimation for Permutation Entropy in time series with tied values, arXiv e-prints, arXiv:1707.01517 (2017).26. D. Cuesta-Frau, M. Varela-Entrecanales, A. Molina-Picó and B. Vargas, Patterns with equal values in permutation entropy: Do they really matter for biosignal classification?, Complexity, 2018 (2018), 1-15.29. D. Cuesta-Frau, A. Molina-Picó, B. Vargas and P. González, Permutation entropy: Enhancing discriminating power by using relative frequencies vector of ordinal patterns instead of their shannon entropy, Entropy, 21 (2019).30. H. Azami and J. Escudero, Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation, Comput. Meth. Program. Biomed., 128 (2016), 40-51.32. G. Manis, M. Aktaruzzaman and R. Sassi, Bubble entropy: An entropy almost free of parameters, IEEE Transact. Biomed. Eng., 64 (2017), 2711-2718.34. L. Zunino, F. Olivares, F. Scholkmann and O. A. Rosso, Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions, Phys. Lett. A, 381 (2017), 1883-1892.38. D. E. Lake, J. S. Richman, M. P. Griffin and J. R. Moorman, Sample entropy analysis of neonatal heart rate variability, Am. J. Physiology-Regulatory Integrat. Comparat. Physiol., 283 (2002), R789-R797, PMID: 12185014.41. I. Unal, Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach, Comput. Math. Methods Med., 2017 (2017), 14.47. A. K. Jain, M. N. Murty and P. J. Flynn, Data clustering: A review, ACM Comput. Surv., 31 (1999), 264-323.51. J. Sander, M. Ester, H.-P. Kriegel and X. Xu, Density-based clustering in spatial databases: The algorithm gdbscan and its applications, Data Min. Knowl. Discov., 2 (1998), 169-194.52. J. Wu, Advances in K-means Clustering: A Data Mining Thinking, Springer Publishing Company, Incorporated, 2012.53. S. Panda, S. Sahu, P. Jena and S. Chattopadhyay, Comparing fuzzy-c means and k-means clustering techniques: A comprehensive study, in Advances in Computer Science, Engineering & Applications (eds. D. C. Wyld, J. Zizka and D. Nagamalai), Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, 451-460.54. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, 101 (2000), 215-220.58. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C. E. Elger, Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64 (2001), 061907.60. N. Iyengar, C. K. Peng, R. Morin, A. L. Goldberger and L. A. Lipsitz, Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics, Am. J. Physiology-Regulatory Integrat. Comparat. Physiol., 271 (1996), R1078-R1084, PMID: 8898003

A Wavelet Based Multiscale Weighted Permutation Entropy Method for Sensor Fault Feature Extraction and Identification

Sensor is the core module in signal perception and measurement applications. Due to the harsh external environment, aging, and so forth, sensor easily causes failure and unreliability. In this paper, three kinds of common faults of single sensor, bias, drift, and stuck-at, are investigated. And a fault diagnosis method based on wavelet permutation entropy is proposed. It takes advantage of the multiresolution ability of wavelet and the internal structure complexity measure of permutation entropy to extract fault feature. Multicluster feature selection (MCFS) is used to reduce the dimension of feature vector, and a three-layer back-propagation neural network classifier is designed for fault recognition. The experimental results show that the proposed method can effectively identify the different sensor faults and has good classification and recognition performance

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Advances in Electrocardiograms

Electrocardiograms have become one of the most important, and widely used medical tools for diagnosing diseases such as cardiac arrhythmias, conduction disorders, electrolyte imbalances, hypertension, coronary artery disease and myocardial infarction. This book reviews recent advancements in electrocardiography. The four sections of this volume, Cardiac Arrhythmias, Myocardial Infarction, Autonomic Dysregulation and Cardiotoxicology, provide comprehensive reviews of advancements in the clinical applications of electrocardiograms. This book is replete with diagrams, recordings, flow diagrams and algorithms which demonstrate the possible future direction for applying electrocardiography to evaluating the development and progression of cardiac diseases. The chapters in this book describe a number of unique features of electrocardiograms in adult and pediatric patient populations with predilections for cardiac arrhythmias and other electrical abnormalities associated with hypertension, coronary artery disease, myocardial infarction, sleep apnea syndromes, pericarditides, cardiomyopathies and cardiotoxicities, as well as innovative interpretations of electrocardiograms during exercise testing and electrical pacing

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

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