Support Vector Machine Based Sub-Classification of Arrhythmia Using ECG Signal

2020 ◽  
Author(s):  
Mohit Singh Dhaka ◽  
Poras Khetarpal ◽  
Neeraj Kumar
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal

scholarly journals Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

2018 ◽  
Vol 970 ◽  
pp. 012012 ◽  
Author(s):  
Arjon Turnip ◽  
M. Ilham Rizqywan ◽  
Dwi E. Kusumandari ◽  
Mardi Turnip ◽  
Poltak Sihombing
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Arrhythmia Detection

scholarly journals Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

2018 ◽  
Author(s):  
M Ashtiyani ◽  
S Navaei Lavasani ◽  
A Asgharzadeh Alvar ◽  
M R Deevband
Keyword(s):  
Genetic Algorithm ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Cardiac Activity ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Features Selection

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.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification
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