scholarly journals Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

Sensors ◽  
2013 ◽  
Vol 13 (12) ◽  
pp. 16494-16511 ◽  
Author(s):  
Gang Li ◽  
Wan-Young Chung
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Wavelet Analysis ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Driver Drowsiness

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.

Model Based on Support Vector Machine for the Estimation of the Heart Rate Variability

2018 ◽  
pp. 186-194
Author(s):  
Catalina Maria Hernández-Ruiz ◽  
Sergio Andrés Villagrán Martínez ◽  
Johan Enrique Ortiz Guzmán ◽  
Paulo Alonso Gaona Garcia
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Support Vector ◽  
Model Based

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.

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