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.

COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR FEATURE-BASED CLASSIFICATION USING HEART RATE VARIABILITY

2020 ◽  
Vol 32 (02) ◽  
pp. 2050009
Author(s):  
Kirti Tripath ◽  
Harsh Sohal ◽  
Shruti Jain
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Diagnostic System ◽  
Support Vector ◽  
Ecg Signal ◽  
K Nearest Neighbor ◽  
Computer Aided ◽  
Feature Based

This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.

scholarly journals Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5483
Author(s):  
Marisol Martinez-Alanis ◽  
Erik Bojorges-Valdez ◽  
Niels Wessel ◽  
Claudia Lerma
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Area Under The Curve ◽  
Premature Ventricular Complex ◽  
Support Vector ◽  
Auc Value ◽  
Sudden Cardiac Death Risk

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.

Detection and Classification of Watermelon Seeds Exterior Quality Based on LS-SVM Using Machine Vision

2013 ◽  
Vol 278-280 ◽  
pp. 727-730
Author(s):  
Xiai Chen ◽  
Shuang Ke ◽  
Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Machine Vision ◽  
Vision System ◽  
Least Square ◽  
Support Vector ◽  
Machine Vision System ◽  
High Quality ◽  
Surface Irregularities

A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.

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