Discrimination power of spectral and nonlinear heart rate variability features for the identification of congestive heart failure

Recognizing pathological heart rhythm features remains a challenge of cardiovascular research. We adopt a machine learning framework with empirically optimized parameters to distinguish heart failure from healthy condition, emphasizing on spectral and nonlinear features of heart rate variability. Fine-grained spectral power densities of RR intervals emerged as the best discriminating group of features, yielding a classification error rate of 13.6 % when presented at a segment length of 50 minutes.