Statistical feature selection of time-domain HRV parameters significantly improves the accuracy of machine learning classifiers for arrhythmia detection.
The repolarization and depolarization in heart generate electrical signals in the form of an ECG wave. The condition of the heart can be indicated by using Heart Rate Variability (HRV) features. In this work, FIR filter is used at the pre-processing phase for denoising, and then statistical analysis is applied for time-domain HRV feature extraction and selection. This algorithm is evaluated on different records of MIT/BIH Normal Sinus Rhythm and Arrhythmia database. The [Formula: see text]-test implementation in both databases shows that there are significant variations in HRV features, where meanRR and HR have suggestive significant ([Formula: see text]) changes, while maxRR, minRR, maxminRR, and SDNN have strongly significant ([Formula: see text]) changes. To validate the statistical analysis of HRV, feature classification has been done using SVM and kNN classifiers. A significant improvement of 2% and 14.02% has been observed in the overall accuracy of SVM and kNN classifiers after feature selection, respectively. These HRV features can be used for the early prediction of various Cardio-Vascular Diseases (CVD).
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Tripathi et al. (2020) studied this question.
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