Faster Detection of Abnormal Electrocardiogram (ECG) Signals Using Fewer Features of Heart Rate Variability (HRV)

Abstract

To reduce the effect of noise in raw Electrocardiogram (ECG) data for faster detection of cardiac arrhythmia, Heart Rate Variability (HRV) features represent good choices. This work extracted 34 popular Heart Rate Variability (HRV) features based on the MIT-BIH Arrhythmia Database. Combinations of 11 feature selection algorithms and 2 classification algorithms are used to discover the effective features of the abnormal ECG signal detection. The systematic comparisons show that the combination of 34 original features has a stable classification performance for 3 different time windows, i.e., 32 RR-intervals, 5 minutes, and 30 minutes of raw ECG records. It has been discovered that a 10-feature combination (RMSSD, SDNN, CV, TINN, HF, SampEn, SD1/SD2, VAI, ED, and DC) can rapidly classify the arrhythmia and normal state, based on the shortest ECG records (32 RR-intervals). The future work will utilize this combination of features to implement in a portable ECG equipment and clinical Arrhythmia on-line detection.