Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features

The goal of this paper is to assess various combinations of heart rate variability (HRV) features in successful classification of four different cardiac rhythms. The rhythms include: normal, congestive heart failure, supraventricular arrhythmia, and any arrhythmia. We approach the problem of automatic cardiac rhythm classification from HRV by employing several features’ schemes. The schemes are evaluated using the random forest classifier. We extracted a total of 78 linear and nonlinear features. Highest results were achieved for normal/supraventricular arrhythmia classification (93%). A feature scheme consisting of: time domain (SDNN, RMSSD, pNN20, pNN50, HTI), frequency domain (Total PSD, VLF, LF, HF, LF/HF), SD1/SD2 ratio, Fano factor, and Allan factor features demonstrated very high classification accuracy, comparable to the results for all extracted features. Results show that nonlinear features have only minor influence on overall classification accuracy. Keywords— heart rate variability, ECG, linear features, nonlinear features, random forest