Entropy Measures of Irregularity and Complexity for Surface Electrocardiogram Time Series in Patients with Congestive Heart Failure

Congestive heart failure (CHF) remains to be one of the major cardiovascular disorders in the world. Due to the prevalence of CHF related issues, it is prudent to seek out new prognostic predictors that would facilitate the prevention, monitoring, and treatment of the disease on a daily basis. A detection approach using entropy measures extracted from surface electrocardiograms (ECGs) and classification for congestive heart failure (CHF) is presented in this paper. Four different entropies are used: approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PE), and energy entropy (EE). These entropies are employed to evaluate the irregularity and complexity of ECG time series and discuss the viability of recognizing CHF patients from normal subjects. Student’s t-tests and receiver operating characteristic (ROC) plots show that among the four entropies, EE outperforms other three entropies. These tests also indicate the feasibility of using surface ECGs to effectively discriminate CHF patients from normal subjects.