A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System

Atrial fibrillation (AF) is characterized by totally disorganized atrial depolarizations without effective atrial contraction. It is the most common form of cardiac arrhythmia, affecting more than 46.3 million people worldwide and its incidence rate remains increasing. Although AF itself is not life...

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Vydané v:	Applied sciences Ročník 12; číslo 5; s. 2387
Hlavní autori:	Mendez, Milna Maria, Hsu, Min-Chia, Yuan, Jenq-Tay, Lynn, Ke-Shiuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.03.2022
Predmet:	Algorithms Cardiac arrhythmia Classification convolutional neural network Datasets Electrocardiography genetic algorithm Heart rate heart rate variability Neural networks Noise paroxysmal atrial fibrillation poincaré plot Sinuses Wearable computers
ISSN:	2076-3417, 2076-3417
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Abstract	Atrial fibrillation (AF) is characterized by totally disorganized atrial depolarizations without effective atrial contraction. It is the most common form of cardiac arrhythmia, affecting more than 46.3 million people worldwide and its incidence rate remains increasing. Although AF itself is not life-threatening, its complications, such as strokes and heart failure, are lethal. About 25% of paroxysmal AF (PAF) patients become chronic for an observation period of more than one year. For long-term and real-time monitoring, a PAF prediction system was developed with four objectives: (1) high prediction accuracy, (2) fast computation, (3) small data storage, and (4) easy medical interpretations. The system takes a 400-point heart rate variability (HRV) sequence containing no AF episodes as the input and outputs whether the corresponding subject will experience AF episodes in the near future (i.e., 30 min). It first converts an input HRV sequence into four image matrices via extended Poincaré plots to capture inter- and intra-person features. Then, the system employs a convolutional neural network (CNN) to perform feature selection and classification based on the input image matrices. Some design issues of the system, including feature conversion and classifier structure, were formulated as a binary optimization problem, which was then solved via a genetic algorithm (GA). A numerical study involving 6085 400-point HRV sequences excerpted from three PhysioNet databases showed that the developed PAF prediction system achieved 87.9% and 87.2% accuracy on the validation and the testing datasets, respectively. The performance is competitive with that of the leading PAF prediction system in the literature, yet our system is much faster and more intensively tested. Furthermore, from the designed inter-person features, we found that PAF patients often possess lower (~60 beats/min) or higher (~100 beats/min) heart rates than non-PAF subjects. On the other hand, from the intra-person features, we observed that PAF patients often exhibit smaller variations (≤5 beats/min) in heart rate than non-PAF subjects, but they may experience short bursts of large heart rate changes sometimes, probably due to abnormal beats, such as premature atrial beats. The other findings warrant further investigations for their medical implications about the onset of PAF.
AbstractList	Atrial fibrillation (AF) is characterized by totally disorganized atrial depolarizations without effective atrial contraction. It is the most common form of cardiac arrhythmia, affecting more than 46.3 million people worldwide and its incidence rate remains increasing. Although AF itself is not life-threatening, its complications, such as strokes and heart failure, are lethal. About 25% of paroxysmal AF (PAF) patients become chronic for an observation period of more than one year. For long-term and real-time monitoring, a PAF prediction system was developed with four objectives: (1) high prediction accuracy, (2) fast computation, (3) small data storage, and (4) easy medical interpretations. The system takes a 400-point heart rate variability (HRV) sequence containing no AF episodes as the input and outputs whether the corresponding subject will experience AF episodes in the near future (i.e., 30 min). It first converts an input HRV sequence into four image matrices via extended Poincaré plots to capture inter- and intra-person features. Then, the system employs a convolutional neural network (CNN) to perform feature selection and classification based on the input image matrices. Some design issues of the system, including feature conversion and classifier structure, were formulated as a binary optimization problem, which was then solved via a genetic algorithm (GA). A numerical study involving 6085 400-point HRV sequences excerpted from three PhysioNet databases showed that the developed PAF prediction system achieved 87.9% and 87.2% accuracy on the validation and the testing datasets, respectively. The performance is competitive with that of the leading PAF prediction system in the literature, yet our system is much faster and more intensively tested. Furthermore, from the designed inter-person features, we found that PAF patients often possess lower (~60 beats/min) or higher (~100 beats/min) heart rates than non-PAF subjects. On the other hand, from the intra-person features, we observed that PAF patients often exhibit smaller variations (≤5 beats/min) in heart rate than non-PAF subjects, but they may experience short bursts of large heart rate changes sometimes, probably due to abnormal beats, such as premature atrial beats. The other findings warrant further investigations for their medical implications about the onset of PAF.
Author	Hsu, Min-Chia Yuan, Jenq-Tay Mendez, Milna Maria Lynn, Ke-Shiuan
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Cites_doi	10.1161/CIRCULATIONAHA.113.005119 10.1109/TBME.2003.821030 10.1006/cviu.2000.0897 10.1007/BF02478504 10.1016/j.cmpb.2016.07.016 10.1155/2018/9050812 10.1016/S0002-8703(98)70030-4 10.1109/TBME.1985.325532 10.1161/01.CIR.101.23.e215 10.1063/1.166141 10.1097/00000542-199907000-00007 10.1186/1475-925X-8-38 10.1161/CIR.0000000000000659 10.1109/JEEIT.2019.8717369 10.1016/0167-2789(85)90011-9 10.1109/ACCESS.2019.2896880 10.1016/j.compbiomed.2021.104367 10.1007/978-3-642-71001-8_15 10.1007/s11517-006-0119-0 10.1103/RevModPhys.57.617 10.1109/TBME.1976.324577 10.1109/PROC.1966.4634 10.1109/TIT.1976.1055501 10.1007/s10916-018-1088-1 10.1016/S0022-0736(95)80017-4 10.1016/j.cmpb.2010.07.011 10.1016/j.physa.2018.06.022 10.1109/JBHI.2019.2957809 10.1042/cs0910201 10.1103/PhysRevLett.50.346 10.1016/S0010-4825(99)00018-9 10.1109/10.959330 10.18632/aging.101386
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References	Goldberger (ref_32) 2000; 101 Benjamin (ref_3) 2019; 139 Sessa (ref_10) 2018; 10 Chugh (ref_2) 2014; 129 ref_14 Wolf (ref_25) 1985; 16 Mohebbi (ref_40) 2012; 105 Park (ref_41) 2009; 8 ref_13 ref_33 Brennan (ref_38) 2001; 48 Jalali (ref_9) 2019; 24 ref_30 Lempel (ref_19) 1976; 22 Liu (ref_37) 2018; 2018 Thakor (ref_36) 1983; 21 DePetrillo (ref_24) 1999; 29 Nassif (ref_29) 2019; 7 ref_16 Wyndham (ref_1) 2000; 27 Anwar (ref_31) 2018; 42 Allan (ref_18) 1966; 54 Eckmann (ref_26) 1985; 57 Pillarisetti (ref_5) 2009; 2 Acharya (ref_15) 2006; 44 Dilaveris (ref_21) 1998; 135 Peng (ref_7) 1995; 5 Parsi (ref_42) 2021; 133 ref_45 ref_22 ref_44 Pan (ref_34) 1985; BME-32 ref_43 Moeslund (ref_28) 2001; 81 Narin (ref_12) 2018; 509 Fano (ref_17) 1947; 72 Goovaerts (ref_35) 1976; 23 Aronow (ref_4) 2009; 1 ref_27 Boon (ref_11) 2016; 134 Kamen (ref_39) 1996; 91 Peng (ref_6) 1995; 28 Grassberger (ref_23) 1983; 50 Thong (ref_8) 2004; 51 Amar (ref_20) 1999; 91
References_xml	– volume: 129 start-page: 837 year: 2014 ident: ref_2 article-title: Worldwide Epidemiology of Atrial Fibrillation: A Global Burden of Disease 2010 Study publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.113.005119 – volume: 27 start-page: 257 year: 2000 ident: ref_1 article-title: Atrial fibrillation: The most common arrhythmia publication-title: Tex. Hear. Inst. J. – volume: 51 start-page: 561 year: 2004 ident: ref_8 article-title: Prediction of Paroxysmal Atrial Fibrillation by Analysis of Atrial Premature Complexes publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2003.821030 – volume: 72 start-page: 26 year: 1947 ident: ref_17 article-title: Ionization Yield of Radiations. II. The Fluctuations of the Number of Ions publication-title: Phys. Rev. (Ser. I) – volume: 81 start-page: 231 year: 2001 ident: ref_28 article-title: A Survey of Computer Vision-Based Human Motion Capture publication-title: Comput. Vis. Image Underst. doi: 10.1006/cviu.2000.0897 – volume: 21 start-page: 343 year: 1983 ident: ref_36 article-title: Optimal QRS detector publication-title: Med Biol. Eng. Comput. doi: 10.1007/BF02478504 – volume: 134 start-page: 187 year: 2016 ident: ref_11 article-title: Paroxysmal atrial fibrillation prediction method with shorter HRV sequences publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2016.07.016 – volume: 2018 start-page: 9050812 year: 2018 ident: ref_37 article-title: Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases publication-title: J. Healthc. Eng. doi: 10.1155/2018/9050812 – volume: 135 start-page: 733 year: 1998 ident: ref_21 article-title: Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation publication-title: Am. Hear. J. doi: 10.1016/S0002-8703(98)70030-4 – volume: 1 start-page: 154 year: 2009 ident: ref_4 article-title: Atrial fibrillation: The new epidemic of the ageing world publication-title: J. Atr. Fibrillation – volume: BME-32 start-page: 230 year: 1985 ident: ref_34 article-title: A Real-Time QRS Detection Algorithm publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.1985.325532 – volume: 101 start-page: e215 year: 2000 ident: ref_32 article-title: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – ident: ref_16 – volume: 5 start-page: 82 year: 1995 ident: ref_7 article-title: Quantification of Scaling Exponents and Crossover Phenomena in Nonstationary Heartbeat Time Series publication-title: CHAOS doi: 10.1063/1.166141 – volume: 91 start-page: 16 year: 1999 ident: ref_20 article-title: Signal-averaged P-wave Duration Does Not Predict Atrial Fibrillation after Thoracic Surgery publication-title: J. Am. Soc. Anesthesiol. doi: 10.1097/00000542-199907000-00007 – volume: 8 start-page: 38 year: 2009 ident: ref_41 article-title: Atrial fibrillation detection by heart rate variability in Poincare plot publication-title: Biomed. Eng. Online doi: 10.1186/1475-925X-8-38 – ident: ref_14 – volume: 139 start-page: e56 year: 2019 ident: ref_3 article-title: Heart disease and stroke statistics—2019 update: A report from the American heart association publication-title: Circulation doi: 10.1161/CIR.0000000000000659 – ident: ref_30 doi: 10.1109/JEEIT.2019.8717369 – ident: ref_44 – volume: 16 start-page: 285 year: 1985 ident: ref_25 article-title: Determining Lyapunov exponents from a time series publication-title: Phys. D Nonlinear Phenom. doi: 10.1016/0167-2789(85)90011-9 – volume: 7 start-page: 19143 year: 2019 ident: ref_29 article-title: Speech Recognition Using Deep Neural Networks: A Systematic Review publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2896880 – volume: 133 start-page: 104367 year: 2021 ident: ref_42 article-title: Prediction of paroxysmal atrial fibrillation using new heart rate variability features publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104367 – ident: ref_22 doi: 10.1007/978-3-642-71001-8_15 – volume: 44 start-page: 1031 year: 2006 ident: ref_15 article-title: Heart rate variability: A review publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-006-0119-0 – volume: 57 start-page: 617 year: 1985 ident: ref_26 article-title: Ergodic theory of chaos and strange attractors publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.57.617 – volume: 23 start-page: 154 year: 1976 ident: ref_35 article-title: A Digital QRS Detector Based on the Principle of Contour Limiting publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.1976.324577 – volume: 54 start-page: 221 year: 1966 ident: ref_18 article-title: Statistics of atomic frequency standards publication-title: Proc. IEEE doi: 10.1109/PROC.1966.4634 – volume: 2 start-page: 191 year: 2009 ident: ref_5 article-title: Evolution of paroxysmal atrial fibrillation to persistent or permanent atrial fibrillation: Predictors of progression publication-title: J. Atr. Fibrillation – volume: 22 start-page: 75 year: 1976 ident: ref_19 article-title: On the complexity of finite sequences publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1976.1055501 – volume: 42 start-page: 226 year: 2018 ident: ref_31 article-title: Medical Image Analysis using Convolutional Neural Networks: A Review publication-title: J. Med Syst. doi: 10.1007/s10916-018-1088-1 – volume: 28 start-page: 59 year: 1995 ident: ref_6 article-title: Fractal mechanisms and heart rate dynamics: Long-range correlations and their breakdown with disease publication-title: J. Electrocardiol. doi: 10.1016/S0022-0736(95)80017-4 – ident: ref_33 – ident: ref_27 – volume: 105 start-page: 40 year: 2012 ident: ref_40 article-title: Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2010.07.011 – volume: 509 start-page: 56 year: 2018 ident: ref_12 article-title: Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability publication-title: Phys. A Stat. Mech. Its Appl. doi: 10.1016/j.physa.2018.06.022 – volume: 24 start-page: 407 year: 2019 ident: ref_9 article-title: Atrial Fibrillation Prediction with Residual Network Using Sensitivity and Orthogonality Constraints publication-title: IEEE J. Biomed. Health Informatics doi: 10.1109/JBHI.2019.2957809 – ident: ref_13 – volume: 91 start-page: 201 year: 1996 ident: ref_39 article-title: Poincaré Plot of Heart Rate Variability Allows Quantitative Display of Parasympathetic Nervous Activity in Humans publication-title: Clin. Sci. doi: 10.1042/cs0910201 – volume: 50 start-page: 346 year: 1983 ident: ref_23 article-title: Characterization of Strange Attractors publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.50.346 – volume: 29 start-page: 393 year: 1999 ident: ref_24 article-title: Determining the Hurst exponent of fractal time series and its application to electrocardiographic analysis publication-title: Comput. Biol. Med. doi: 10.1016/S0010-4825(99)00018-9 – ident: ref_45 – ident: ref_43 – volume: 48 start-page: 1342 year: 2001 ident: ref_38 article-title: Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.959330 – volume: 10 start-page: 166 year: 2018 ident: ref_10 article-title: Heart rate variability as predictive factor for sudden cardiac death publication-title: Aging doi: 10.18632/aging.101386
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Snippet	Atrial fibrillation (AF) is characterized by totally disorganized atrial depolarizations without effective atrial contraction. It is the most common form of...
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SubjectTerms	Algorithms Cardiac arrhythmia Classification convolutional neural network Datasets Electrocardiography genetic algorithm Heart rate heart rate variability Neural networks Noise paroxysmal atrial fibrillation poincaré plot Sinuses Wearable computers
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Title	A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
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