Wavelet based fluctuation analysis on ECG time series

Abstract

In this paper the correlation behavior and multifractal properties of electrocardiogram (ECG) signals were investigated through the wavelet based fluctuation analysis method to classify patients and healthy subjects. For this purpose the ECG time series data of five patients suffering from congestive heart failure (CHF) and five healthy human-being were obtained from Physionet online database. From the results, we observe that the presence of persistent behavior and strong multifractal nature in all the time series. Also we found that the calculated Hurst scaling exponents distinguishes the healthy signals from patients with CHF. We suggest that this approach may be useful for diagnosis and prognosis of heart disease.