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Nardelli M, Citi L, Barbieri R, Valenza G. Characterization of autonomic states by complex sympathetic and parasympathetic dynamics. Physiol Meas 2023; 44. [PMID: 36787644 DOI: 10.1088/1361-6579/acbc07] [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: 11/15/2022] [Accepted: 02/14/2023] [Indexed: 02/16/2023]
Abstract
Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.
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Affiliation(s)
- Mimma Nardelli
- Bioengineering and Robotics Research Centre E. Piaggio and Dipartimento di Ingegneria dell'Informazione, University of Pisa, Italy
| | - Luca Citi
- School of Computer Science and Electronic Engineering, University of Essex, United Kingdom
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E. Piaggio and Dipartimento di Ingegneria dell'Informazione, University of Pisa, Italy
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2
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Duangburong S, Phruksaphanrat B, Muengtaweepongsa S. Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities. APPLIED SCIENCES 2023; 13:1712. [DOI: 10.3390/app13031712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for local patients. Therefore, this research collected ECG signals from 60 local Thai patients (age 52.53 ± 23.92) to create a classification model. The coefficient of variance (CV), the median absolute deviation (MAD), and the root mean square of the successive differences (RMSSD) are ordinary feature variables of RR irregularities used by existing models. The square of average variation (SAV) is a newly proposed feature that extracts from the irregularity of RR intervals. All variables were found to be statistically different using ANOVA tests and Tukey’s method with a p-value less than 0.05. The methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were also tested and compared to find the best classification model. Finally, SAV showed the best performance using the ANFIS model with trapezoidal membership function, having the highest system accuracy (ACC) at 89.33%, sensitivity (SE), specificity (SP), and positive predictivity (PPR) for NSR at 100.00%, 94.00%, and 89.29%, PAF at 88.00%, 90.57%, and 81.48%, and AF at 80.00%, 96.00%, and 90.91%, respectively.
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Affiliation(s)
- Suttirak Duangburong
- Research Unit in Industrial Statistics and Operational Research, Industrial Engineering Department, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12121, Thailand
| | - Busaba Phruksaphanrat
- Research Unit in Industrial Statistics and Operational Research, Industrial Engineering Department, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12121, Thailand
| | - Sombat Muengtaweepongsa
- Center of Excellence in Stroke, Faculty of Medicine, Thammasat University, Pathum Thani 10121, Thailand
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Nardelli M, Citi L, Barbieri R, Valenza G. Intrinsic Complexity of Sympathetic and Parasympathetic Dynamics from HRV series: a Preliminary Study on Postural Changes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2577-2580. [PMID: 33018533 DOI: 10.1109/embc44109.2020.9175587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The analysis of complex heartbeat dynamics has been widely used to characterize heartbeat autonomic control in healthy and pathological conditions. However, underlying physiological correlates of complexity measurements from heart rate variability (HRV) series have not been identified yet. To this extent, we investigated intrinsic irregularity and complexity of cardiac sympathetic and vagal activity time series during postural changes. We exploited our recently proposed HRV-based, time-varying Sympathetic and Parasympathetic Activity Indices (SAI and PAI) and performed Sample Entropy, Fuzzy Entropy, and Distribution Entropy calculations on publicly-available heartbeat series gathered from 10 healthy subjects undergoing resting state and passive slow tilt sessions. Results show significantly higher entropy values during the upright position than resting state in both SAI and PAI series. We conclude that an increase in HRV complexity resulting from postural changes may derive from sympathetic and vagal activities with higher complex dynamics.
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Pérez-Gutiérrez MF, Sánchez-Muñoz JJ, Erazo-Rodas M, Guerrero-Curieses A, Everss E, Quesada-Dorador A, Ruiz-Granell R, Ibáñez-Criado A, Bellver-Navarro A, Rojo-Álvarez JL, García-Alberola A. Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20154162. [PMID: 32726931 PMCID: PMC7435921 DOI: 10.3390/s20154162] [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: 06/20/2020] [Revised: 07/19/2020] [Accepted: 07/22/2020] [Indexed: 06/11/2023]
Abstract
Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IVTM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF.
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Affiliation(s)
- Milton Fabricio Pérez-Gutiérrez
- Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador;
| | - Juan José Sánchez-Muñoz
- Arrhythmia Unit and Electrophysiology, Department of Cardiology, Virgen de la Arrixaca University Hospital, Instituto Murciano de Investigación Biosanitaria, 30120 Murcia, Spain; (J.J.S.-M.); (A.G.-A.)
| | - Mayra Erazo-Rodas
- Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador;
| | - Alicia Guerrero-Curieses
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain; (A.G.-C.); (E.E.); (J.L.R.-Á.)
| | - Estrella Everss
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain; (A.G.-C.); (E.E.); (J.L.R.-Á.)
| | - Aurelio Quesada-Dorador
- Arrhythmia Unit, Department of Cardiology, Hospital General de Valencia, 46014 Valencia, Spain;
| | - Ricardo Ruiz-Granell
- Arrhythmia Unit, Department of Cardiology, Hospital Clínico Universitario, Av. Blasco Ibañez, 17, 46010 Valencia, Spain;
| | - Alicia Ibáñez-Criado
- Arrhythmia Unit, Department of Cardiology, Hospital Clínico de Alicante, 03010 Alicante, Spain;
| | | | - José Luis Rojo-Álvarez
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain; (A.G.-C.); (E.E.); (J.L.R.-Á.)
| | - Arcadi García-Alberola
- Arrhythmia Unit and Electrophysiology, Department of Cardiology, Virgen de la Arrixaca University Hospital, Instituto Murciano de Investigación Biosanitaria, 30120 Murcia, Spain; (J.J.S.-M.); (A.G.-A.)
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Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion. ENTROPY 2020; 22:e22070748. [PMID: 33286519 PMCID: PMC7517291 DOI: 10.3390/e22070748] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/01/2020] [Accepted: 07/02/2020] [Indexed: 01/10/2023]
Abstract
Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20-30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3-9% and other previously proposed predictors of ECV by 15-30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.
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Roonizi EK, Sassi R. An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation. IEEE J Biomed Health Inform 2017; 21:1573-1580. [DOI: 10.1109/jbhi.2016.2625338] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Corino VDA, Laureanti R, Ferranti L, Scarpini G, Lombardi F, Mainardi LT. Detection of atrial fibrillation episodes using a wristband device. Physiol Meas 2017; 38:787-799. [DOI: 10.1088/1361-6579/aa5dd7] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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8
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Corino VD, Sandberg F, Lombardi F, Mainardi LT, Sörnmo L. Atrioventricular nodal function during atrial fibrillation: Model building and robust estimation. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.10.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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9
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Donoso FI, Figueroa RL, Lecannelier EA, Pino EJ, Rojas AJ. Atrial activity selection for atrial fibrillation ECG recordings. Comput Biol Med 2013; 43:1628-36. [DOI: 10.1016/j.compbiomed.2013.08.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 08/02/2013] [Accepted: 08/05/2013] [Indexed: 11/27/2022]
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10
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An Atrioventricular Node Model for Analysis of the Ventricular Response During Atrial Fibrillation. IEEE Trans Biomed Eng 2011; 58:3386-95. [DOI: 10.1109/tbme.2011.2166262] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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11
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Lemay M, Vesin JM, Jacquemet V, Forclaz A, Kappenberger L, van Oosterom A. Spatial dynamics of atrial activity assessed by the vectorcardiogram: from sinus rhythm to atrial fibrillation. ACTA ACUST UNITED AC 2007; 9 Suppl 6:vi109-18. [DOI: 10.1093/europace/eum215] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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