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Keshmiri S, Tomonaga S, Mizutani H, Doya K. Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human. Comput Biol Med 2024; 173:108335. [PMID: 38564855 DOI: 10.1016/j.compbiomed.2024.108335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.
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Affiliation(s)
- Soheil Keshmiri
- Optical Neuroimaging Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.
| | - Sutashu Tomonaga
- Neural Computation Unit (NCU), Okinawa Institute of Science and Technology, Okinawa, Japan.
| | - Haruo Mizutani
- Suntory Global Innovation Center Limited (SGIC), Suntory, Kyoto, Japan.
| | - Kenji Doya
- Neural Computation Unit (NCU), Okinawa Institute of Science and Technology, Okinawa, Japan.
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2
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Menesse G, Houben AM, Soriano J, Torres JJ. Integrated information decomposition unveils major structural traits of in silico and in vitro neuronal networks. CHAOS (WOODBURY, N.Y.) 2024; 34:053139. [PMID: 38809907 DOI: 10.1063/5.0201454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/06/2024] [Indexed: 05/31/2024]
Abstract
The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition (Φ-ID), allow one to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics, and information. Here, we apply Φ-ID on in silico and in vitro data to decompose the usual transfer entropy measure into different modes of information transfer, namely, synergistic, redundant, or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.
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Affiliation(s)
- Gustavo Menesse
- Department of Electromagnetism and Physics of the Matter & Institute Carlos I for Theoretical and Computational Physics, University of Granada, 18071 Granada, Spain
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Asunción, 111451 San Lorenzo, Paraguay
| | - Akke Mats Houben
- Departament de Física de la Matèria Condensada, Universitat de Barcelona and Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona and Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Joaquín J Torres
- Department of Electromagnetism and Physics of the Matter & Institute Carlos I for Theoretical and Computational Physics, University of Granada, 18071 Granada, Spain
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3
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Volpes G, Valenti S, Genova G, Barà C, Parisi A, Faes L, Busacca A, Pernice R. Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements. BIOSENSORS 2024; 14:205. [PMID: 38667198 PMCID: PMC11048376 DOI: 10.3390/bios14040205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.
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Affiliation(s)
| | | | | | | | | | | | | | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy; (G.V.); (S.V.); (G.G.); (C.B.); (A.P.); (L.F.); (A.B.)
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4
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Andrzejewska M, Wróblewski T, Cygan S, Ozimek M, Petelczyc M. From physiological complexity to data interactions-A case study of recordings from exercise monitoring. CHAOS (WOODBURY, N.Y.) 2024; 34:043136. [PMID: 38619248 DOI: 10.1063/5.0178750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/22/2024] [Indexed: 04/16/2024]
Abstract
The popularity of nonlinear analysis has been growing simultaneously with the technology of effort monitoring. Therefore, considering the simple methods of physiological data collection and the approaches from the information domain, we proposed integrating univariate and bivariate analysis for the rest and effort comparison. Two sessions separated by an intensive training program were studied. Nine subjects participated in the first session (S1) and seven in the second session (S2). The protocol included baseline (BAS), exercise, and recovery phase. During all phases, electrocardiogram (ECG) was recorded. For the analysis, we selected corresponding data lengths of BAS and exercise usually lasting less than 5 min. We found the utility of the differences between original data and their surrogates for sample entropy Sdiff and Kullback-Leibler divergence KLDdiff. Sdiff of heart rate variability was negative in BAS and exercise but its sensitivity for phases discrimination was not satisfactory. We studied the bivariate analysis of RR intervals and corresponding QT peaks by Interlayer Mutual Information (IMI) and average edge overlap (AVO) markers. While the IMI parameter decreases in exercise conditions, AVO increased in effort compared to BAS. These findings conclude that researchers should consider a bivariate analysis of extracted RR intervals and corresponding QT datasets, when only ECG is recorded during tests.
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Affiliation(s)
| | - Tomasz Wróblewski
- Faculty of Physics, Warsaw University of Technology, 00-662 Warsaw, Poland
| | - Szymon Cygan
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland
| | - Mateusz Ozimek
- Faculty of Physics, Warsaw University of Technology, 00-662 Warsaw, Poland
| | - Monika Petelczyc
- Faculty of Physics, Warsaw University of Technology, 00-662 Warsaw, Poland
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Rostaghi M, Rostaghi S, Humeau-Heurtier A, Rajji TK, Azami H. NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107941. [PMID: 38006684 DOI: 10.1016/j.cmpb.2023.107941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND AND OBJECTIVE We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data. METHODS NLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions. This toolbox combines state-of-the-art nonlinear dynamical techniques with advanced multivariate entropy methods, providing users with powerful analytical capabilities. NLDyn enables analyses with or without a sliding window, and users can easily access and customize default parameters. RESULTS NLDyn generates results that are both exportable and visually informative, facilitating seamless integration into research and presentations. Its ongoing development ensures it remains at the forefront of nonlinear dynamics analysis. CONCLUSIONS NLDyn is a valuable resource for researchers in the biomedical field, offering an intuitive interface and a wide array of nonlinear analysis tools. Its integration of advanced techniques empowers users to gain deeper insights from their data. As we continually refine and expand NLDyn's capabilities, we envision it becoming an indispensable tool for the exploration of complex dynamics in biomedical signals.
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Affiliation(s)
- Mostafa Rostaghi
- Modal Analysis Research Laboratory, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran
| | - Sadegh Rostaghi
- Department of Mechanical Engineering, Naghshejahan Higher Education Institute, Isfahan, Iran
| | | | - Tarek K Rajji
- Centre for Addiction and Mental Health, University of Toronto, Toronto Dementia Research Alliance, Toronto, ON, Canada
| | - Hamed Azami
- Centre for Addiction and Mental Health, University of Toronto, Toronto Dementia Research Alliance, Toronto, ON, Canada.
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6
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Rosenblum M, Pikovsky A. Inferring connectivity of an oscillatory network via the phase dynamics reconstruction. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1298228. [PMID: 38073862 PMCID: PMC10704096 DOI: 10.3389/fnetp.2023.1298228] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/13/2023] [Indexed: 06/10/2024]
Abstract
We review an approach for reconstructing oscillatory networks' undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network's coupling matrix in the first approximation in the coupling strength.
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Affiliation(s)
- Michael Rosenblum
- Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany
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Santos DOC, Trindade MAS, da Silva AJ. Nonextensive realizations in interacting ion channels: Implications for mechano-electrical transducer mechanisms. Biosystems 2023; 232:105005. [PMID: 37611860 DOI: 10.1016/j.biosystems.2023.105005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/12/2023] [Accepted: 08/19/2023] [Indexed: 08/25/2023]
Abstract
We propose a theoretical model to investigate the thermodynamics of single and coupled two-state ion channels, associated with mechanoelectrical transduction (MET) and hair cell biophysics. The modeling was based on the Tsallis nonextensive statistical mechanics. The choice for a nonextensive framework in modeling ion channels is encouraged on the fact that we take into account the presence of interactions or long-range correlations in the dynamics of single and coupled ion channels. However, the basic assumptions that support Boltzmann-Gibbs statistics, traditionally used to model ion channel dynamics, state that the system is formed by independent or weakly interacting elements. Despite being well studied in many biological systems, the literature has not yet addressed the study of both entropy and mutual information related to isolated or physically interacting pairs of MET channels. Inspired by hair cell biophysics, we show how the presence of nonextensivity, or subadditivity and superadditivity modulates the nonextensive entropy and mutual information as functions of stereocilia displacements. We also observe that the magnitude of the interaction between the two channels, given by a nonextensive parameter, influences the amplitude of the nonextensive joint entropy and mutual information as functions of the hair cell displacements. Finally, we show how nonextensivity regulates the current versus displacement curve for a single and a pair of interacting two-state channels. The present findings shed light on the thermodynamic process involved in the molecular mechanisms of the auditory system.
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Affiliation(s)
- D O C Santos
- Universidade Federal do Sul da Bahia, CEP 45600-923, Itabuna, Bahia, Brazil
| | - M A S Trindade
- Colegiado de Física, Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia, CEP 41150-000, Salvador, Bahia, Brazil
| | - A J da Silva
- Universidade Federal do Sul da Bahia, CEP 45600-923, Itabuna, Bahia, Brazil.
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8
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Köse HY, İkizoğlu S. Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1385. [PMID: 37895507 PMCID: PMC10606935 DOI: 10.3390/e25101385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual's walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.
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Affiliation(s)
- Harun Yaşar Köse
- Department of Mechatronics Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Istanbul, Türkiye;
| | - Serhat İkizoğlu
- Department of Control and Automation Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Istanbul, Türkiye
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9
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Yao W, Yao W, Wang J. Threshold distribution of equal states for quantitative amplitude fluctuations. Physiol Meas 2023; 44:095004. [PMID: 37666257 DOI: 10.1088/1361-6579/acf6a6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/04/2023] [Indexed: 09/06/2023]
Abstract
Objective. The distribution of equal states (DES) quantifies amplitude fluctuations in biomedical signals. However, under certain conditions, such as a high resolution of data collection or special signal processing techniques, equal states may be very rare, whereupon the DES fails to measure the amplitude fluctuations.Approach. To address this problem, we develop a novel threshold DES (tDES) that measures the distribution of differential states within a threshold. To evaluate the proposed tDES, we first analyze five sets of synthetic signals generated in different frequency bands. We then analyze sleep electroencephalography (EEG) datasets taken from the public PhysioNet.Main results. Synthetic signals and detrend-filtered sleep EEGs have no neighboring equal values; however, tDES can effectively measure the amplitude fluctuations within these data. The tDES of EEG data increases significantly as the sleep stage increases, even with datasets covering very short periods, indicating decreased amplitude fluctuations in sleep EEGs. Generally speaking, the presence of more low-frequency components in a physiological series reflects smaller amplitude fluctuations and larger DES.Significance. The tDES provides a reliable computing method for quantifying amplitude fluctuations, exhibiting the characteristics of conceptual simplicity and computational robustness. Our findings broaden the application of quantitative amplitude fluctuations and contribute to the classification of sleep stages based on EEG data.
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Affiliation(s)
- Wenpo Yao
- School of Geographic and Biologic Information, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, People's Republic of China
| | - Wenli Yao
- State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Jun Wang
- School of Geographic and Biologic Information, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
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10
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Platiša MM, Radovanović NN, Pernice R, Barà C, Pavlović SU, Faes L. Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1072. [PMID: 37510019 PMCID: PMC10378632 DOI: 10.3390/e25071072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Abstract
The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (GC), Transfer Entropy (TE) and Cross Entropy (CE), to quantify the directed coupling and causality between cardiac (RR interval) and respiratory (Resp) time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the RR (high number of ventricular extrasystoles) and in the Resp time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that GC as a linear model measure is not sensitive to both nonlinear components and only model free measures as TE and CE may quantify them. CRT responders mainly exhibit unchanged asymmetry in the TE values, with statistically significant dominance of the information flow from Resp to RR over the opposite flow from RR to Resp, before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.
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Affiliation(s)
- Mirjana M Platiša
- Laboratory for Biosignals, Institute of Biophysics, Faculty of Medicine, University of Belgrade, Višegradska 26-2, 11000 Belgrade, Serbia
| | - Nikola N Radovanović
- Pacemaker Center, University Clinical Center of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Chiara Barà
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Siniša U Pavlović
- Pacemaker Center, University Clinical Center of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
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11
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Mijatovic G, Bara C, Pernice R, Loncar-Turukalo T, Nollo G, Faes L. Exploring the Short-Term Memory of Heart Rate Variability through Model-Free Information Measures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083690 DOI: 10.1109/embc40787.2023.10341158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In this work, we perform a comparative analysis of discrete- and continuous-time estimators of information-theoretic measures quantifying the concept of memory utilization in short-term heart rate variability (HRV). Specifically, considering heartbeat intervals in discrete time we compute the measure of information storage (IS) and decompose it into immediate memory utilization (IMU) and longer memory utilization (MU) terms; considering the timings of heartbeats in continuous time we compute the measure of MU rate (MUR). All measures are computed through model-free approaches based on nearest neighbor entropy estimators applied to the HRV series of a group of 15 healthy subjects measured at rest and during postural stress. We find, moving from rest to stress, statistically significant increases of the IS and the IMU, as well as of the MUR. Our results suggest that both discrete-time and continuous-time approaches can detect the higher predictive capacity of HRV occurring with postural stress, and that such increased memory utilization is due to fast mechanisms likely related to sympathetic activation.
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12
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Wang Q, Yao W, Bai D, Yi W, Yan W, Wang J. Schizophrenia MEG Network Analysis Based on Kernel Granger Causality. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1006. [PMID: 37509953 PMCID: PMC10378589 DOI: 10.3390/e25071006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023]
Abstract
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.
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Affiliation(s)
- Qiong Wang
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China
| | - Wenpo Yao
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Dengxuan Bai
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wanyi Yi
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wei Yan
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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13
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Hull A, Morton JB. Activity-State Entropy: A Novel Brain Entropy Measure Based on Spatial Patterns of Activity. J Neurosci Methods 2023; 393:109868. [PMID: 37120138 DOI: 10.1016/j.jneumeth.2023.109868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND Brain entropy is a measure of the complexity of brain activity that has been linked to various cognitive abilities. The measure is based on Shannon Entropy, a measure from Information Theory that quantifies the information capacity of a system from the probability distribution of its states. Most fMRI studies measure brain entropy at the voxel level as time-series entropy and assume that entropic time-series indicate complex large-scale spatiotemporal patterns of activity. New Method We developed a novel measure of brain entropy called Activity-State Entropy. The method quantifies entropy based on underlying patterns of coactivation identified using Principal Components Analysis. These patterns, termed eigenactivity states, combine in time-varying proportions. RESULTS We showed that Activity-State Entropy is sensitive to the complexity of the spatiotemporal patterns of activity in simulated fMRI data. We then applied this measure to real resting-state fMRI data and found that the eigenactivity states that explained the most variance in the data were comprised of large clusters of coactivating voxels, including clusters within Default Mode Network regions. More entropic brains were increasingly influenced by eigenactivity states comprised of smaller and more sparsely distributed clusters. Comparison to Existing Methods We compared Activity-State Entropy to Sample Entropy and Dispersion Entropy, two time-series entropy measures commonly used in neuroimaging research, and found all three measures were positively correlated. CONCLUSIONS Activity-State Entropy provides a measure of the spatiotemporal complexity of brain activity that complements time-series based measures of brain entropy.
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Affiliation(s)
- Adam Hull
- Undergraduate Program in Neuroscience, Western University, London, Canada, N6A 3K7.
| | - J Bruce Morton
- Department of Psychology, Western University, London, Canada, N6A 3K7.
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14
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Barà C, Sparacino L, Pernice R, Antonacci Y, Porta A, Kugiumtzis D, Faes L. Comparison of discretization strategies for the model-free information-theoretic assessment of short-term physiological interactions. CHAOS (WOODBURY, N.Y.) 2023; 33:033127. [PMID: 37003789 DOI: 10.1063/5.0140641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/17/2023] [Indexed: 06/19/2023]
Abstract
This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures of the dynamic coupling between short realizations of random processes. The measures considered are the mutual information rate (MIR) between two random processes X and Y and the terms of its decomposition evidencing either the individual entropy rates of X and Y and their joint entropy rate, or the transfer entropies from X to Y and from Y to X and the instantaneous information shared by X and Y. All measures are estimated through discretization of the random variables forming the processes, performed either via uniform quantization (binning approach) or rank ordering (permutation approach). The binning and permutation approaches are compared on simulations of two coupled non-identical Hènon systems and on three datasets, including short realizations of cardiorespiratory (CR, heart period and respiration flow), cardiovascular (CV, heart period and systolic arterial pressure), and cerebrovascular (CB, mean arterial pressure and cerebral blood flow velocity) measured in different physiological conditions, i.e., spontaneous vs paced breathing or supine vs upright positions. Our results show that, with careful selection of the estimation parameters (i.e., the embedding dimension and the number of quantization levels for the binning approach), meaningful patterns of the MIR and of its components can be achieved in the analyzed systems. On physiological time series, we found that paced breathing at slow breathing rates induces less complex and more coupled CR dynamics, while postural stress leads to unbalancing of CV interactions with prevalent baroreflex coupling and to less complex pressure dynamics with preserved CB interactions. These results are better highlighted by the permutation approach, thanks to its more parsimonious representation of the discretized dynamic patterns, which allows one to explore interactions with longer memory while limiting the curse of dimensionality.
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Affiliation(s)
- Chiara Barà
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
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15
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Volpes G, Barà C, Busacca A, Stivala S, Javorka M, Faes L, Pernice R. Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures. SENSORS (BASEL, SWITZERLAND) 2022; 22:9149. [PMID: 36501850 PMCID: PMC9739824 DOI: 10.3390/s22239149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time-domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices.
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Affiliation(s)
- Gabriele Volpes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Chiara Barà
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Alessandro Busacca
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Salvatore Stivala
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Michal Javorka
- Department of Physiology, Jessenius Faculty of Medicine, Comenius University, 036 01 Martin, Slovakia
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
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16
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Azami H, Sanei S, Rajji TK. Ensemble entropy: A low bias approach for data analysis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Loke LHL, Chisholm RA. Measuring habitat complexity and spatial heterogeneity in ecology. Ecol Lett 2022; 25:2269-2288. [PMID: 35977844 PMCID: PMC9804605 DOI: 10.1111/ele.14084] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/03/2022] [Accepted: 07/09/2022] [Indexed: 01/05/2023]
Abstract
Habitat complexity has been considered a key driver of biodiversity and other ecological phenomena for nearly a century. However, there is still no consensus over the definition of complexity or how to measure it. Up-to-date and clear guidance on measuring complexity is urgently needed, particularly given the rise of remote sensing and advent of technologies that allow environments to be scanned at unprecedented spatial extents and resolutions. Here we review how complexity is measured in ecology. We provide a framework for metrics of habitat complexity, and for the related concept of spatial heterogeneity. We focus on the two most commonly used complexity metrics in ecology: fractal dimension and rugosity. We discuss the pros and cons of these metrics using practical examples from our own empirical data and from simulations. Fractal dimension is particularly widely used, and we provide a critical examination of it drawing on research from other scientific fields. We also discuss informational metrics of complexity and their potential benefits. We chart a path forward for research on measuring habitat complexity by presenting, as a guide, sets of essential and desirable criteria that a metric of complexity should possess. Lastly, we discuss the applied significance of our review.
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Affiliation(s)
- Lynette H. L. Loke
- School of Natural Sciences, Faculty of Science and EngineeringMacquarie UniversityNorth RydeNew South WalesAustralia
| | - Ryan A. Chisholm
- Department of Biological SciencesNational University of SingaporeSingapore CitySingapore
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18
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Sebastiani L, Mastorci F, Magrini M, Paradisi P, Pingitore A. Synchronization between music dynamics and heart rhythm is modulated by the musician’s emotional involvement: A single case study. Front Psychol 2022; 13:908488. [PMID: 36160502 PMCID: PMC9493261 DOI: 10.3389/fpsyg.2022.908488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/15/2022] [Indexed: 11/22/2022] Open
Abstract
In this study we evaluated heart rate variability (HRV) changes in a pianist, playing in a laboratory, to investigate whether HRV changes are guided by music temporal features or by technical difficulty and/or subjective factors (e.g., experienced effort). The pianist was equipped with a wearable telemetry device for ECG recording during the execution of 4 classical and 5 jazz pieces. From ECG we derived the RR intervals series (tachogram), and, for each piece, analyzed HRV in the time (RR, RMSSD, Stress Index) and frequency domains (Total spectral power) and performed non-linear analysis (Multiscale Entropy). We also studied the correlation (Pearson) between the time course of music volume envelope and tachogram. Results showed a general reduction of parasympathetic and an increase of sympathetic activity, with the greatest changes during the classical pieces execution, the pianist appraised as more demanding than the jazz ones. The most marked changes occurred during the most technically/emotionally demanding piece, and correlation analysis revealed a negative association between music volume envelope time course and tachogram only for this piece, suggesting a modulation of the limbic system on the synchronization between heart rhythm and music temporal features. Classical music was also associated with the increase of entropy (1st scale) with respect to rest, indicating its effectiveness in driving flexible, healthy, heart dynamics. In conclusion, HRV seems modulated not only by the music temporal features, but also by the pianist’s emotional involvement, which is greatly influenced, in a non-trivial manner, by the technical demands and musician expertise.
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Affiliation(s)
- Laura Sebastiani
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
- Institute of Information Science and Technologies “Alessandro Faedo”, National Research Council (ISTI-CNR), Pisa, Italy
| | - Francesca Mastorci
- Institute of Clinical Physiology, National Research Council (IFC-CNR), Pisa, Italy
| | - Massimo Magrini
- Institute of Information Science and Technologies “Alessandro Faedo”, National Research Council (ISTI-CNR), Pisa, Italy
| | - Paolo Paradisi
- Institute of Information Science and Technologies “Alessandro Faedo”, National Research Council (ISTI-CNR), Pisa, Italy
- Basque Center for Applied Mathematics (BCAM), Bilbao, Spain
- *Correspondence: Paolo Paradisi,
| | - Alessandro Pingitore
- Institute of Clinical Physiology, National Research Council (IFC-CNR), Pisa, Italy
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Makowski D, Te AS, Pham T, Lau ZJ, Chen SHA. The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2. ENTROPY 2022; 24:e24081036. [PMID: 36010700 PMCID: PMC9407071 DOI: 10.3390/e24081036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/22/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023]
Abstract
Complexity quantification, through entropy, information theory and fractal dimension indices, is gaining a renewed traction in psychophsyiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. Using the NeuroKit2 Python software, we computed a list of 112 (predominantly used) complexity indices on signals varying in their characteristics (noise, length and frequency spectrum). We then systematically compared the indices by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. Based on these considerations, we propose that a selection of 12 indices, together representing 85.97% of the total variance of all indices, might offer a parsimonious and complimentary choice in regards to the quantification of the complexity of time series. Our selection includes CWPEn, Line Length (LL), BubbEn, MSWPEn, MFDFA (Max), Hjorth Complexity, SVDEn, MFDFA (Width), MFDFA (Mean), MFDFA (Peak), MFDFA (Fluctuation), AttEn. Elements of consideration for alternative subsets are discussed, and data, analysis scripts and code for the figures are open-source.
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Affiliation(s)
- Dominique Makowski
- School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore; (A.S.T.); (T.P.); (Z.J.L.)
- Correspondence: (D.M.); (S.H.A.C.)
| | - An Shu Te
- School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore; (A.S.T.); (T.P.); (Z.J.L.)
| | - Tam Pham
- School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore; (A.S.T.); (T.P.); (Z.J.L.)
| | - Zen Juen Lau
- School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore; (A.S.T.); (T.P.); (Z.J.L.)
| | - S. H. Annabel Chen
- School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore; (A.S.T.); (T.P.); (Z.J.L.)
- LKC Medicine, Nanyang Technological University, Singapore 639818, Singapore
- National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
- Centre for Research and Development in Learning, Nanyang Technological University, Singapore 639818, Singapore
- Correspondence: (D.M.); (S.H.A.C.)
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20
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Pinto H, Pernice R, Silva ME, Javorka M, Faes L, Rocha AP. Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular control. Physiol Meas 2022; 43. [PMID: 35853449 DOI: 10.1088/1361-6579/ac826c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/19/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. APPROACH We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and synergistic contributions, is obtained using a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This novel approach allows to quantify the directed information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations among the analyzed processes. Additionally, it provides analytical expressions for the computation of the information measures, by exploiting the theory of state space models. The approach is first illustrated in simulated VARFI processes and then applied to H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. MAIN RESULTS We demonstrate the ability of the VARFI modeling approach to account for the coexistence of short-term and long-range correlations in the study of multivariate processes. Physiologically, we show that postural stress induces larger redundant and synergistic effects from S and R to H at short time scales, while mental stress induces larger information transfer from S to H at longer time scales, thus evidencing the different nature of the two stressors. SIGNIFICANCE The proposed methodology allows to extract useful information about the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems, which cannot be observed using standard methods that do not consider long-range correlations.
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Affiliation(s)
- Hélder Pinto
- Universidade do Porto Faculdade de Ciencias, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal, Porto, 4169-007, PORTUGAL
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Maria Eduarda Silva
- Universidade do Porto Faculdade de Economia, R. Dr. Roberto Frias 464, Porto, Porto, Porto, 4200-464, PORTUGAL
| | - Michal Javorka
- Department of Physiology, Comenius University in Bratislava Jessenius Faculty of Medicine in Martin, Malá hora 4A, 036 01 Martin-Záturčie, Martin, 036 01, SLOVAKIA
| | - Luca Faes
- DEIM, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Ana Paula Rocha
- Universidade do Porto Faculdade de Ciencias, Rua do Campo Alegre s/n, 4169-007 Porto, Porto, Porto, 4169-007, PORTUGAL
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21
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Pinto H, Dias C, Rocha AP. Multiscale Information Decomposition of Long Memory Processes: Application to Plateau Waves of Intracranial Pressure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1753-1756. [PMID: 36085854 DOI: 10.1109/embc48229.2022.9870925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Traumatic Brain Injury (TBI) patients present high levels of physical stress, which in some situations can manifest as Plateau Wave (PW) episodes. This intense stress phenomenon can be evidenced by Heart Rate Variability (HRV). Thus, the multivariate and simultaneous analysis of cardio-cerebrovascular oscillations, involving the RR intervals, mean arterial pressure (MAP) and the amplitude of intracranial pressure (AMP), will be useful to understand the interconnections between body signals, allowing the interpretation of the combined activity of pathophysiological mechanisms. In this work, the multiscale representation of the Transfer Entropy (TE) and of its decomposition in the network of these three interacting processes is obtained, based on a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This method allows to assess directed interactions and to quantify the information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations. The results show that the baseline RR, but not MAP can provide information about the possibility of a PW arising. During PW, the long-term correlations highlight synergistic interactions between MAP and AMP processes on RR. The multiscale decomposition of the information along with the incorporation of the long term correlations allowed a better description of HRV during PW, highlighting the fact that the HRV mirrors this cerebrovascular phenomena.
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22
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Mijatovic G, Kljajic D, Kasas-Lazetic K, Milutinov M, Stivala S, Busacca A, Cino AC, Stramaglia S, Faes L. Information Dynamics of Electric Field Intensity before and during the COVID-19 Pandemic. ENTROPY 2022; 24:e24050726. [PMID: 35626609 PMCID: PMC9140641 DOI: 10.3390/e24050726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/08/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
Abstract
This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors.
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Affiliation(s)
- Gorana Mijatovic
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Dragan Kljajic
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Karolina Kasas-Lazetic
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Miodrag Milutinov
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Salvatore Stivala
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
| | - Alessandro Busacca
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
| | - Alfonso Carmelo Cino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
| | | | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
- Correspondence:
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23
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Nonlinear directed information flow estimation for fNIRS brain network analysis based on the modified multivariate transfer entropy. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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24
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Ferraz MSA, Kihara AH. Beyond randomness: Evaluating measures of information entropy in binary series. Phys Rev E 2022; 105:044101. [PMID: 35590660 DOI: 10.1103/physreve.105.044101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/09/2022] [Indexed: 06/15/2023]
Abstract
The enormous amount of currently available data demands efforts to extract meaningful information. For this purpose, different measurements are applied, including Shannon's entropy, permutation entropy, and the Lempel-Ziv complexity. These methods have been used in many applications, such as pattern recognition, series classification, and several other areas (e.g., physical, financial, and biomedical). Data in these applications are often presented in binary series with temporal correlations. Herein, we compare the measures of information entropy in binary series conveying short- and long-range temporal correlations characterized by the Hurst exponent H. Combining numerical and analytical approaches, we scrutinize different methods that were not efficient in detecting temporal correlations. To surpass this limitation, we propose a measure called the binary permutation index (BPI). We will demonstrate that BPI efficiently discriminates patterns embedded in the series, offering advantages over previous methods. Subsequently, we collect stock market time series and rain precipitation data as well as perform in vivo electrophysiological recordings in the hippocampus of an experimental animal model of temporal lobe epilepsy, in which the BPI application in both public open source and experimental data is demonstrated. An index is proposed to evaluate information entropy, allowing the ability to discriminate randomness and extract meaningful information in binary time series.
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Affiliation(s)
- Mariana Sacrini Ayres Ferraz
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo, Brazil
| | - Alexandre Hiroaki Kihara
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo, Brazil
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Abid N, Mani AR. The mechanistic and prognostic implications of heart rate variability analysis in patients with cirrhosis. Physiol Rep 2022; 10:e15261. [PMID: 35439350 PMCID: PMC9017982 DOI: 10.14814/phy2.15261] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023] Open
Abstract
Chronic liver damage leads to scarring of the liver tissue and ultimately a systemic illness known as cirrhosis. Patients with cirrhosis exhibit multi-organ dysfunction and high mortality. Reduced heart rate variability (HRV) is a hallmark of cirrhosis, reflecting a state of defective cardiovascular control and physiological network disruption. Several lines of evidence have revealed that decreased HRV holds prognostic information and can predict survival of patients independent of the severity of liver disease. Thus, the aim of this review is to shed light on the mechanistic and prognostic implications of HRV analysis in patients with cirrhosis. Notably, several studies have extensively highlighted the critical role systemic inflammation elicits in conferring the reduction in patients' HRV. It appears that IL-6 is likely to play a central mechanistic role, whereby its levels also correlate with manifestations, such as autonomic neuropathy and hence the partial uncoupling of the cardiac pacemaker from autonomic control. Reduced HRV has also been reported to be highly correlated with the severity of hepatic encephalopathy, potentially through systemic inflammation affecting specific brain regions, involved in both cognitive function and autonomic regulation. In general, the prognostic ability of HRV analysis holds immense potential in improving survival rates for patients with cirrhosis, as it may indeed be added to current prognostic indicators, to ultimately increase the accuracy of selecting the recipient most in need of liver transplantation. However, a network physiology approach in the future is critical to delineate the exact mechanistic basis by which decreased HRV confers poor prognosis.
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Affiliation(s)
- Noor‐Ul‐Hoda Abid
- Network Physiology LabDivision of MedicineUCLLondonUK
- Lancaster Medical SchoolLancaster UniversityLancasterUK
| | - Ali R. Mani
- Network Physiology LabDivision of MedicineUCLLondonUK
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26
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Zhang X, Li J, Cai Z, Zhao L, Liu C. Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection. Front Physiol 2022; 13:890139. [PMID: 35431981 PMCID: PMC9012152 DOI: 10.3389/fphys.2022.890139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to be confused with the rhythm of atrial fibrillation. Therefore, in this article, we designed a screening algorithm for single premature beat, multi premature beats, bigeminy and trigeminy premature beats, according to their rhythm characteristics to reduce false detection caused by premature beats during the PAF detection process. The proposed elimination method was verified on ECG segments with different types of premature beats, and tested on long-term ECG data of PAF patients. ECG segments of different kinds of premature beats were selected from MIT Atrial Fibrillation database (MIT-AFDB), MIT-BIH Arrhythmia database (MIT-AR) and wearable ECG data from the China Physiological Signal Challenge 2021 (CPSC 2021). The proposed method can effectively eliminate single premature beat segments with 99.5% accuracy, and it also can eliminate more than 95% of ECG segments with other types of premature beats. We designed PAF-score as a new index to evaluate the accuracy of detection, and we also calculate the misjudged and missed segments to comprehensively evaluate the PAF detection algorithm. The proposed method get a PAF-score of 0.912 on MIT-AFDB. The proposed method also has the potential to implant low computing power wearable devices for real-time analysis.
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Affiliation(s)
| | - Jianqing Li
- *Correspondence: Jianqing Li, ; Chengyu Liu,
| | | | | | - Chengyu Liu
- *Correspondence: Jianqing Li, ; Chengyu Liu,
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Araújo NS, Reyes-Garcia SZ, Brogin JAF, Bueno DD, Cavalheiro EA, Scorza CA, Faber J. Chaotic and stochastic dynamics of epileptiform-like activities in sclerotic hippocampus resected from patients with pharmacoresistant epilepsy. PLoS Comput Biol 2022; 18:e1010027. [PMID: 35417449 PMCID: PMC9037954 DOI: 10.1371/journal.pcbi.1010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 04/25/2022] [Accepted: 03/16/2022] [Indexed: 11/30/2022] Open
Abstract
The types of epileptiform activity occurring in the sclerotic hippocampus with highest incidence are interictal-like events (II) and periodic ictal spiking (PIS). These activities are classified according to their event rates, but it is still unclear if these rate differences are consequences of underlying physiological mechanisms. Identifying new and more specific information related to these two activities may bring insights to a better understanding about the epileptogenic process and new diagnosis. We applied Poincaré map analysis and Recurrence Quantification Analysis (RQA) onto 35 in vitro electrophysiological signals recorded from slices of 12 hippocampal tissues surgically resected from patients with pharmacoresistant temporal lobe epilepsy. These analyzes showed that the II activity is related to chaotic dynamics, whereas the PIS activity is related to deterministic periodic dynamics. Additionally, it indicates that their different rates are consequence of different endogenous dynamics. Finally, by using two computational models we were able to simulate the transition between II and PIS activities. The RQA was applied to different periods of these simulations to compare the recurrences between artificial and real signals, showing that different ranges of regularity-chaoticity can be directly associated with the generation of PIS and II activities. Temporal lobe epilepsy (TLE) is the most prevalent type of epilepsy in adults and hippocampal sclerosis is the major pathophysiological substrate of pharmaco-refractory TLE. Different patterns of epileptiform-like activity have been described in human hippocampal sclerosis, but the standard analysis applied to characterize the activities usually do not consider the nonlinear features that epileptiform patterns exhibit. Here, using Poincaré map and Recurrence Quantitative Analysis we characterized the most prevalent type of epileptiform-like activities—interictal-like events (II) and periodic ictal spiking (PIS), recorded in vitro from resected hippocampi of pharmacoresistant patients with TLE—according to their levels of stochasticity, chaoticity and determinism. The II activities showed to be more chaotic with complex rhythmicity than PIS activities. The nonlinear dynamic differences between II and PIS leads us to conjecture that they are expressions of different seizure susceptibility. We also identified that each hippocampal subfield expresses II and PIS activities in a specific and different way. Finally, from the modulation of internal parameters of two computational models, we show the conversion of one type of activity into the other, showing how specific neuron networks synchronize over time, leading to II and PIS activities and then into a generalized seizure.
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Affiliation(s)
- Noemi S. Araújo
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Selvin Z. Reyes-Garcia
- Departamento de Ciencias Morfológicas, Facultad de Ciencias Médicas, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
| | - João A. F. Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Douglas D. Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Esper A. Cavalheiro
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Carla A. Scorza
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
- * E-mail:
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Casagrande A, Fabris F, Girometti R. Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests. Med Biol Eng Comput 2022; 60:941-955. [PMID: 35195818 PMCID: PMC8863911 DOI: 10.1007/s11517-021-02494-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 12/17/2021] [Indexed: 11/28/2022]
Abstract
Since 1948, Shannon theoretic methods for modeling information have found a wide range of applications in several areas where information plays a key role, which goes well beyond the original scopes for which they have been conceived, namely data compression and error correction over a noisy channel. Among other uses, these methods have been applied in the broad field of medical diagnostics since the 1970s, to quantify diagnostic information, to evaluate diagnostic test performance, but also to be used as technical tools in image processing and registration. This review illustrates the main contributions in assessing the accuracy of diagnostic tests and the agreement between raters, focusing on diagnostic test performance measurements and paired agreement evaluation. This work also presents a recent unified, coherent, and hopefully, final information-theoretical approach to deal with the flows of information involved among the patient, the diagnostic test performed to appraise the state of disease, and the raters who are checking the test results. The approach is assessed by considering two case studies: the first one is related to evaluating extra-prostatic cancers; the second concerns the quality of rapid tests for COVID-19 detection.
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Affiliation(s)
- Alberto Casagrande
- Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Trieste, Italy
| | - Francesco Fabris
- Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, Trieste, Italy.
| | - Rossano Girometti
- Istituto di Radiologia, Dipartimento di Area Medica, Università degli Studi di Udine, Ospedale S. Maria della Misericordia, Udine, Italy
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Seri R, Martinoli M. Asymptotic Properties of the Plug-in Estimator of the Discrete Entropy Under Dependence. IEEE TRANSACTIONS ON INFORMATION THEORY 2021; 67:7659-7683. [DOI: 10.1109/tit.2021.3109307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Forecasting COVID-19 infections in the Arabian Gulf region. MODELING EARTH SYSTEMS AND ENVIRONMENT 2021; 8:3813-3822. [PMID: 34778510 PMCID: PMC8571680 DOI: 10.1007/s40808-021-01332-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/22/2021] [Indexed: 11/19/2022]
Abstract
In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method. The results also indicate that the poorer forecast performance of long short-term memory neural networks occurs in particular when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes. One important result of this study is the possible relationship between data complexity and forecast accuracy with different models as suggested in the entropy analysis. It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data.
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Flood MW, Grimm B. EntropyHub: An open-source toolkit for entropic time series analysis. PLoS One 2021; 16:e0259448. [PMID: 34735497 PMCID: PMC8568273 DOI: 10.1371/journal.pone.0259448] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/18/2021] [Indexed: 11/24/2022] Open
Abstract
An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.
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Affiliation(s)
- Matthew W. Flood
- Human Motion, Orthopaedics, Sports Medicine and Digital Methods (HOSD), Luxembourg Institute of Health (LIH), Eich, Luxembourg
| | - Bernd Grimm
- Human Motion, Orthopaedics, Sports Medicine and Digital Methods (HOSD), Luxembourg Institute of Health (LIH), Eich, Luxembourg
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Pernice R, Volpes G, Krohova JC, Javorka M, Busacca A, Faes L. Feasibility of Linear Parametric Estimation of Dynamic Information Measures to assess Physiological Stress from Short-Term Cardiovascular Variability . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:290-293. [PMID: 34891293 DOI: 10.1109/embc46164.2021.9630697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Extensive efforts have been recently devoted to implement fast and reliable algorithms capable of assessing the physiological response of the organism to physiological stress. In this study, we propose the comparison between model-free and linear parametric methods as regards their ability to detect alterations in the dynamics and in the complexity of cardiovascular and respiratory variability evoked by postural and mental stress. Dynamic entropy (DE) and information storage (IS) measures were calculated on three physiological time-series, i.e. heart period, respiratory volume and systolic arterial pressure, on 61 healthy subjects monitored in resting conditions as well as during head-up tilt and while performing a mental arithmetic task. The results of the comparison suggest the feasibility of DE and IS measures computed from different physiological signals to discriminate among resting and stress states. If compared to the model-free algorithm, the faster linear method appears to be capable of detecting the same (or even more) statistically significant variations of DE or IS between resting and stress conditions, being thus in perspective more suitable for the integration within wearable devices. The computation of entropy indices extracted from multiple physiological signals acquired through wearables will allow a real-time stress assessment on people in daily-life situations.
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Abstract
Parkinson’s disease (PD) is a type of neurodegenerative diseases. PD influences gait in many aspects: reduced gait speed and step length, increased axial rigidity, and impaired rhythmicity. Gait-related data used in this study are from PhysioNet. Twenty-one PD patients and five healthy controls (CO) were sorted into four groups: PD without task (PDw), PD with dual task (PDd), control without task (COw), and control with dual task (COd). Since dual task actions are attention demanding, either gait or cognitive function may be affected. To quantify the used walking data, eight pressure sensors installed in each insole are used to measure the vertical ground reaction force. Thus, quantitative measurement analysis is performed utilizing multiscale entropy (MSE) and complexity index (CI) to analyze and differentiate between the ground reaction force of the four different groups. Results show that the CI of patients with PD is higher than that of CO and 11 of the sensor signals are statistically significant (p < 0.05). The COd group has larger CI values at the beginning (p = 0.021) but they get lower at the end of the test (p = 0.000) compared to that in the COw group. The end-of-test CI for the PDw group is lower in one of the feet sensor signals, and in the right total ground reaction force compared to the PDd group counterparts. In conclusion, when people start to adjust their gait due to pathology or stress, CI may increase first and reach a peak, but it decreases afterward when stress or pathology is further increased.
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Pernice R, Sparacino L, Nollo G, Stivala S, Busacca A, Faes L. Comparison of frequency domain measures based on spectral decomposition for spontaneous baroreflex sensitivity assessment after Acute Myocardial Infarction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102680] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Bottaro M, Abid NUH, El-Azizi I, Hallett J, Koranteng A, Formentin C, Montagnese S, Mani AR. Skin temperature variability is an independent predictor of survival in patients with cirrhosis. Physiol Rep 2021; 8:e14452. [PMID: 32562383 PMCID: PMC7305245 DOI: 10.14814/phy2.14452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/22/2020] [Accepted: 04/27/2020] [Indexed: 12/18/2022] Open
Abstract
Background Cirrhosis is a disease with multisystem involvement. It has been documented that patients with cirrhosis exhibit abnormal patterns of fluctuation in their body temperature. However, the clinical significance of this phenomenon is not well understood. The aim of this study was to determine if temperature variability analysis can predict survival in patients with cirrhosis. Methods Thirty eight inpatients with cirrhosis were enrolled in the study. Wireless temperature sensors were used to record patients’ proximal skin temperature for 24 hr. The pattern of proximal temperature fluctuation was assessed using the extended Poincaré plot to measure short‐term and long‐term proximal temperature variability (PTV). Patients were followed up for 12 months, and information was collected on the occurrence of death/liver transplantation. Results During the follow‐up period, 15 patients (39%) died or underwent transplantation for hepatic decompensation. Basal proximal skin temperature absolute values were comparable in survivors and nonsurvivors. However, nonsurvivors showed a significant reduction in both short‐term and long‐term HRV indices. Cox regression analysis showed that both short‐term and long‐term PTV indices could predict survival in these patients. However, only measures of short‐term PTV were shown to be independent of the severity of hepatic failure in predicting survival. Finally, the prognostic value of short‐term PTV was also independent of heart rate variability, that is, a measure of autonomic dysfunction. Conclusion Changes in the pattern of patients’ temperature fluctuations, rather than their absolute values, hold key prognostic information, suggesting that impaired thermoregulation may play an important role in the pathophysiology of cirrhosis.
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Affiliation(s)
- Matteo Bottaro
- Department of Medicine, University of Padova, Padova, Italy
| | | | - Ilias El-Azizi
- Network Physiology Lab, Division of Medicine, UCL, London, UK
| | - Joseph Hallett
- Network Physiology Lab, Division of Medicine, UCL, London, UK
| | - Anita Koranteng
- Network Physiology Lab, Division of Medicine, UCL, London, UK
| | | | | | - Ali R Mani
- Network Physiology Lab, Division of Medicine, UCL, London, UK
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Chatain C, Ramdani S, Vallier JM, Gruet M. Recurrence quantification analysis of force signals to assess neuromuscular fatigue in men and women. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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37
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Perinelli A, Castelluzzo M, Tabarelli D, Mazza V, Ricci L. Relationship between mutual information and cross-correlation time scale of observability as measures of connectivity strength. CHAOS (WOODBURY, N.Y.) 2021; 31:073106. [PMID: 34340343 DOI: 10.1063/5.0053857] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
The task of identifying and characterizing network structures out of experimentally observed time series is tackled by implementing different solutions, ranging from entropy-based techniques to the evaluation of the significance of observed correlation estimators. Among the metrics that belong to the first class, mutual information is of major importance due to the relative simplicity of implementation and its relying on the crucial concept of entropy. With regard to the second class, a method that allows us to assess the connectivity strength of a link in terms of a time scale of its observability via the significance estimate of measured cross correlation was recently shown to provide a reliable tool to study network structures. In this paper, we investigate the relationship between this last metric and mutual information by simultaneously assessing both metrics on large sets of data extracted from three experimental contexts, human brain magnetoencephalography, human brain electroencephalography, and surface wind measurements carried out on a small regional scale, as well as on simulated coupled, auto-regressive processes. We show that the relationship is well described by a power law and provide a theoretical explanation based on a simple noise and signal model. Besides further upholding the reliability of cross-correlation time scale of observability, the results show that the combined use of this metric and mutual information can be used as a valuable tool to identify and characterize connectivity links in a wide range of experimental contexts.
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Affiliation(s)
- Alessio Perinelli
- CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
| | | | - Davide Tabarelli
- CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
| | - Veronica Mazza
- CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
| | - Leonardo Ricci
- CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
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Carroll TL. Optimizing Reservoir Computers for Signal Classification. Front Physiol 2021; 12:685121. [PMID: 34220549 PMCID: PMC8249854 DOI: 10.3389/fphys.2021.685121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Reservoir computers are a type of recurrent neural network for which the network connections are not changed. To train the reservoir computer, a set of output signals from the network are fit to a training signal by a linear fit. As a result, training of a reservoir computer is fast, and reservoir computers may be built from analog hardware, resulting in high speed and low power consumption. To get the best performance from a reservoir computer, the hyperparameters of the reservoir computer must be optimized. In signal classification problems, parameter optimization may be computationally difficult; it is necessary to compare many realizations of the test signals to get good statistics on the classification probability. In this work, it is shown in both a spiking reservoir computer and a reservoir computer using continuous variables that the optimum classification performance occurs for the hyperparameters that maximize the entropy of the reservoir computer. Optimizing for entropy only requires a single realization of each signal to be classified, making the process much faster to compute.
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Ozimek M, Żebrowski JJ, Baranowski R. Information Flow Between Heart Rhythm, Repolarization, and the Diastolic Interval Series for Healthy Individuals and LQTS1 Patients. Front Physiol 2021; 12:611731. [PMID: 34163369 PMCID: PMC8215390 DOI: 10.3389/fphys.2021.611731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Using information theoretic measures, relations between heart rhythm, repolarization in the tissue of the heart, and the diastolic interval time series are analyzed. These processes are a fragment of the cardiovascular physiological network. A comparison is made between the results for 84 (42 women) healthy individuals and 65 (45 women) long QT syndrome type 1 (LQTS1) patients. Self-entropy, transfer entropy, and joint transfer entropy are calculated for the three time series and their combinations. The results for self-entropy indicate the well-known result that regularity of heart rhythm for healthy individuals is larger than that of QT interval series. The flow of information depends on the direction with the flow from the heart rhythm to QT dominating. In LQTS1 patients, however, our results indicate that information flow in the opposite direction may occur—a new result. The information flow from the heart rhythm to QT dominates, which verifies the asymmetry seen by Porta et al. in the variable tilt angle experiment. The amount of new information and self-entropy for LQTS1 patients is smaller than that for healthy individuals. However, information transfers from RR to QT and from DI to QT are larger in the case of LQTS1 patients.
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Affiliation(s)
- Mateusz Ozimek
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Jan J Żebrowski
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Rafał Baranowski
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland.,Institute of Cardiology, Warszawa-Anin, Poland
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Lazic I, Pernice R, Loncar-Turukalo T, Mijatovic G, Faes L. Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics. ENTROPY 2021; 23:e23060698. [PMID: 34073121 PMCID: PMC8227407 DOI: 10.3390/e23060698] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/26/2023]
Abstract
Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.
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Affiliation(s)
- Ivan Lazic
- Department of Power, Electronic and Communication Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia;
- Correspondence: (I.L.); (T.L.-T.)
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (R.P.); (L.F.)
| | - Tatjana Loncar-Turukalo
- Department of Power, Electronic and Communication Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia;
- Correspondence: (I.L.); (T.L.-T.)
| | - Gorana Mijatovic
- Department of Power, Electronic and Communication Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia;
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (R.P.); (L.F.)
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Bouny P, Arsac LM, Touré Cuq E, Deschodt-Arsac V. Entropy and Multifractal-Multiscale Indices of Heart Rate Time Series to Evaluate Intricate Cognitive-Autonomic Interactions. ENTROPY 2021; 23:e23060663. [PMID: 34070402 PMCID: PMC8230296 DOI: 10.3390/e23060663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 12/17/2022]
Abstract
Recent research has clarified the existence of a networked system involving a cortical and subcortical circuitry regulating both cognition and cardiac autonomic control, which is dynamically organized as a function of cognitive demand. The main interactions span multiple temporal and spatial scales and are extensively governed by nonlinear processes. Hence, entropy and (multi)fractality in heart period time series are suitable to capture emergent behavior of the cognitive-autonomic network coordination. This study investigated how entropy and multifractal-multiscale analyses could depict specific cognitive-autonomic architectures reflected in the heart rate dynamics when students performed selective inhibition tasks. The participants (N=37) completed cognitive interference (Stroop color and word task), action cancellation (stop-signal) and action restraint (go/no-go) tasks, compared to watching a neutral movie as baseline. Entropy and fractal markers (respectively, the refined composite multiscale entropy and multifractal-multiscale detrended fluctuation analysis) outperformed other time-domain and frequency-domain markers of the heart rate variability in distinguishing cognitive tasks. Crucially, the entropy increased selectively during cognitive interference and the multifractality increased during action cancellation. An interpretative hypothesis is that cognitive interference elicited a greater richness in interactive processes that form the central autonomic network while action cancellation, which is achieved via biasing a sensorimotor network, could lead to a scale-specific heightening of multifractal behavior.
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Affiliation(s)
- Pierre Bouny
- Univ. Bordeaux, CNRS, Laboratoire IMS, UMR 5218 Talence, France; (L.M.A.); (V.D.-A.)
- URGOTECH, 15 avenue d’Iéna, 75116 Paris, France;
- Correspondence:
| | - Laurent M. Arsac
- Univ. Bordeaux, CNRS, Laboratoire IMS, UMR 5218 Talence, France; (L.M.A.); (V.D.-A.)
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Keshmiri S. Conditional Entropy: A Potential Digital Marker for Stress. ENTROPY (BASEL, SWITZERLAND) 2021; 23:286. [PMID: 33652891 PMCID: PMC7996836 DOI: 10.3390/e23030286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.
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Affiliation(s)
- Soheil Keshmiri
- Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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Ribeiro M, Henriques T, Castro L, Souto A, Antunes L, Costa-Santos C, Teixeira A. The Entropy Universe. ENTROPY 2021; 23:e23020222. [PMID: 33670121 PMCID: PMC7916845 DOI: 10.3390/e23020222] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 11/16/2022]
Abstract
About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy's strengths and of limitations is essential to ensure the proper improvement of this research field.
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Affiliation(s)
- Maria Ribeiro
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal;
- Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
- Correspondence:
| | - Teresa Henriques
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (T.H.); (L.C.); (C.C.-S.); (A.T.)
- Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
| | - Luísa Castro
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (T.H.); (L.C.); (C.C.-S.); (A.T.)
| | - André Souto
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisboa, Portugal;
- Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
| | - Luís Antunes
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal;
- Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Cristina Costa-Santos
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (T.H.); (L.C.); (C.C.-S.); (A.T.)
- Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
| | - Andreia Teixeira
- Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal; (T.H.); (L.C.); (C.C.-S.); (A.T.)
- Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
- Instituto Politécnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal
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Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection. Med Biol Eng Comput 2021; 59:165-173. [PMID: 33387183 DOI: 10.1007/s11517-020-02292-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 11/22/2020] [Indexed: 10/22/2022]
Abstract
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF detection from dynamic ECGs. In this study, we explored two potential training strategies to address the over-fitting problem in AF detection. The first one is to use the Fast Fourier transform (FFT) and Hanning-window-based filter to suppress the influence from individual difference. Another is to train the model on the wearable ECG data to improve the robustness of model. Wearable ECG data from 29 patients with arrhythmia were collected for at least 24 h. To verify the effectiveness of the training strategies, a Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN)-based model was proposed and tested. We tested the model on the independent wearable ECG data set, as well as the MIT-BIH Atrial Fibrillation database and PhysioNet/Computing in Cardiology Challenge 2017 database. The model achieved 96.23%, 95.44%, and 95.28% accuracy rates on the three databases, respectively. Pertaining to the comparison of the accuracy rates on each training set, the accuracy of the model trained in conjunction with the proposed training strategies only reduced by 2%, while the accuracy of the model trained without the training strategies decreased by approximately 15%. Therefore, the proposed training strategies serve as effective mechanisms for devising a robust AF detector and significantly enhanced the detection accuracy rates of the resulting deep networks.
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Lehnertz K, Bröhl T, Rings T. The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges. Front Physiol 2020; 11:598694. [PMID: 33408639 PMCID: PMC7779628 DOI: 10.3389/fphys.2020.598694] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 11/30/2020] [Indexed: 12/30/2022] Open
Abstract
The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
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46
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Shahsavari Baboukani P, Graversen C, Alickovic E, Østergaard J. Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1124. [PMID: 33286893 PMCID: PMC7597255 DOI: 10.3390/e22101124] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/22/2020] [Accepted: 09/28/2020] [Indexed: 12/31/2022]
Abstract
We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data.
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Affiliation(s)
| | - Carina Graversen
- Eriksholm Research Centre, Oticon A/S, 3070 Snekkersten, Denmark; (C.G.); (E.A.)
| | - Emina Alickovic
- Eriksholm Research Centre, Oticon A/S, 3070 Snekkersten, Denmark; (C.G.); (E.A.)
- Department of Electrical Engineering, Linköping University, 581 83 Linköping, Sweden
| | - Jan Østergaard
- Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark;
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Zhou J, Gu X, Gu C, Yang H, Weng T, Rohling JHT. Cellular coupling determines scale-invariant behavior of neurons in suprachiasmatic nucleus. Chronobiol Int 2020; 37:1669-1676. [PMID: 32967468 DOI: 10.1080/07420528.2020.1825469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The main clock in mammals, located in the suprachiasmatic nucleus (SCN) of hypothalamus, not only regulates the daily rhythms in physiological and behavioral activities, but also plays a key role as one of the control nodes in the brain regulating behavioral activity. As such, it induces scale-invariance in the temporal patterns of behavioral activity and of multi-unit neural activity of the SCN network. In particular, the scale-invariant patterns maintain across multiple time scales from 3 minutes to 10 hours, characterized by a scaling exponent around 1. Thus far, no study found the origin of the scale-invariance of the SCN network. Using the method of correlation-dependent balance estimation of diffusion entropy (cBEDE), we found that scale-invariance also exists in the individual neurons of the SCN, and the scale invariance properties are significantly increased when the neurons are coupled in a network of neurons. Improved scale invariance in the single neurons is, therefore, imposed by the emergent network properties of the SCN network. Our findings show that the scale-invariance of the SCN can already be found at the level of the individual neurons and that the application of a scale invariance measure, such as cBEDE, can help in determining the network status of the SCN.
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Affiliation(s)
- J Zhou
- Business School, University of Shanghai for Science and Technology , Shanghai, China
| | - X Gu
- Business School, University of Shanghai for Science and Technology , Shanghai, China
| | - C Gu
- Business School, University of Shanghai for Science and Technology , Shanghai, China
| | - H Yang
- Business School, University of Shanghai for Science and Technology , Shanghai, China
| | - T Weng
- Business School, University of Shanghai for Science and Technology , Shanghai, China
| | - J H T Rohling
- Department of Molecular Cell Biology, Laboratory for Neurophysiology, Leiden University Medical Center , Leiden, The Netherlands
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48
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Lucchini M, Pini N, Burtchen N, Signorini MG, Fifer WP. Transfer Entropy Modeling of Newborn Cardiorespiratory Regulation. Front Physiol 2020; 11:1095. [PMID: 32973570 PMCID: PMC7481456 DOI: 10.3389/fphys.2020.01095] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/07/2020] [Indexed: 01/26/2023] Open
Abstract
This study investigates the complex interplay between the cardiac and respiratory systems in 268 healthy neonates born between 35 and 40 weeks of gestation. The aim is to provide a comprehensive description of the developing cardiorespiratory information transfer mechanisms as a function of gestational age (GA). This report proposes an extension of the traditional Transfer Entropy measure (TE), which employs multiple lagged versions of the time series of the intervals between two successive R waves of the QRS signal on the electrocardiogram (RR series) and respiration time series (RESP). The method aims to quantify the instantaneous and delayed effects between the two processes within a fine-grained time scale. Firstly, lagged TE was validated on a simulated dataset. Subsequently, lagged TE was employed on newborn cardiorespiratory data. Results indicate a progressive increase in information transfer as a function of gestational age, as well as significant differences in terms of instantaneous and delayed interactions between the cardiac and the respiratory system when comparing the two TE directionalities (RR→RESP vs. RESP→RR). The proposed investigation addresses the role of the different autonomic nervous system (ANS) branches involved in the cardiorespiratory system, since the sympathetic and parasympathetic branches operate at different time scales. Our results allow to infer that the two TE directionalities are uniquely and differently modulated by both branches of the ANS. TE adds an original quantitative tool to understanding cardiorespiratory imbalance in early infancy.
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Affiliation(s)
- Maristella Lucchini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States.,Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
| | - Nina Burtchen
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Maria G Signorini
- Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
| | - William P Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
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Keshmiri S. Entropy and the Brain: An Overview. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E917. [PMID: 33286686 PMCID: PMC7597158 DOI: 10.3390/e22090917] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/25/2020] [Accepted: 08/19/2020] [Indexed: 12/17/2022]
Abstract
Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks' information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks' information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain's capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.
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Affiliation(s)
- Soheil Keshmiri
- The Thomas N. Sato BioMEC-X Laboratories, Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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50
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Solís-Montufar EE, Gálvez-Coyt G, Muñoz-Diosdado A. Entropy Analysis of RR-Time Series From Stress Tests. Front Physiol 2020; 11:981. [PMID: 32903750 PMCID: PMC7438833 DOI: 10.3389/fphys.2020.00981] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 07/20/2020] [Indexed: 11/14/2022] Open
Abstract
The RR-interval time series or tachograms obtained from electrocardiograms have been widely studied since they reflect the cardiac variability, and this is an indicative of the health status of a person. The tachogram can be seen as a highly non-linear and complex time series, and therefore, should be analyzed with non-linear techniques. In this work, several entropy measures, Sample Entropy (SampEn), Approximate Entropy (ApEn), and Fuzzy Entropy (FuzzyEn) are used as a measure of heart rate variability (HRV). Tachograms belonging to thirty-nine subjects were obtained from a cardiac stress test consisting of a rest period followed by a period of moderate physical activity. Subjects are grouped according to their physical activity using the IPAQ sedentary and active questionnaire, we work with youth and middle-aged adults. The entropy measures for each group show that for the sedentary subjects the values are high at rest and decrease appreciably with moderate physical activity, This happens for both young and middle-aged adults. These results are highly reproducible. In the case of the subjects that exercise regularly, an increase in entropy is observed or they tend to retain the entropy value that they had at rest. It seems that there is a possible correlation between the physical condition of a person with the increase or decrease in entropy during moderate physical activity with respect to the entropy at rest. It was also observed that entropy during longer physical activity tests tends to decrease as fatigue accumulates, but this decrease is small compared to the change that occurs when going from rest to physical activity.
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Affiliation(s)
- Eric E. Solís-Montufar
- Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City, Mexico
- Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Gonzalo Gálvez-Coyt
- Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Alejandro Muñoz-Diosdado
- Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Mexico City, Mexico
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