Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

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ASHTIYANI M.; NAVAEI LAVASANI S.; Asgharzadeh Alvar A.; DEEVBAND M.R.

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Paper Information

Journal: JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING Year:2018 | Volume:8 | Issue:4 Page(s): 423-434

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Title

Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

Author(s)

ASHTIYANI M. | NAVAEI LAVASANI S. | Asgharzadeh Alvar A. | DEEVBAND M.R. | Issue Writer Certificate

Keywords

Heart Rate Variability (HRV)

Wavelet Transform

Genetic Algorithm (GA)

Support Vector Machine (SVM)

Abstract

Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system. Objective: In this study, we aimed to evaluate the efficiency of discrete Wavelet Transform (DWT) based features extracted from HRV which were further selected by genetic algorithm (GA), and were deployed by support vector machine to HRV classification. Materials and Methods: In this paper, 53 ECGs including 3 different beat types (ventricular fibrillation (VF), atrial fibrillation (AF) and also normal sinus rhythm (NSR)), were selected from the MIT/BIH arrhythmia database. The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β ), best features selection by GA and classification of normal and abnormal ECGs using the selected features by support vector machine (SVM). Results: The performance of the classification procedure employing the combination of selected features were evaluated using several measures including accuracy, sensitivity, specificity and precision which resulted in 97. 14%, 97. 54%, 96. 9% and 97. 64%, respectively. Conclusion: A comparative analysis with the related existing methods illustrates the proposed method has a higher potential in the classification of AF and VF. The attempt to classify the ECG signal has been successfully achieved. The proposed method has shown a promising sensitivity of 97. 54% which indicates that this technique is an excellent model for computer-aided diagnosis of cardiac arrhythmias.

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APA: Copy

ASHTIYANI, M., NAVAEI LAVASANI, S., Asgharzadeh Alvar, A., & DEEVBAND, M.R.. (2018). Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING, 8(4), 423-434. SID. https://sid.ir/paper/334639/en

Vancouver: Copy

ASHTIYANI M., NAVAEI LAVASANI S., Asgharzadeh Alvar A., DEEVBAND M.R.. Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING[Internet]. 2018;8(4):423-434. Available from: https://sid.ir/paper/334639/en

IEEE: Copy

M. ASHTIYANI, S. NAVAEI LAVASANI, A. Asgharzadeh Alvar, and M.R. DEEVBAND, “Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm,” JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING, vol. 8, no. 4, pp. 423–434, 2018, [Online]. Available: https://sid.ir/paper/334639/en

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