Multifractal Detrended Fluctuation Analysis of Congestive Heart Failure Disease Based on Constructed Heartbeat Sequence

Abstract

Heart rate variability (HRV) can be used as a common detection method for congestive heart failure (CHF). Existing researches regarding HRV, including both linear indicators and nonlinear characteristics, are mostly based on the RR intervals of the ECG signal. This article proposed a sequence that can reflect the regulation of sympathetic and parasympathetic nerve on heart rate, and on this basis, conducted multifractal detrended fluctuation analysis (MFDFA). We extracted multifractal features to quantitatively compare the complexity of proposed sequence between the healthy and CHF groups. Results showed that abnormal physiological and pathological conditions due to the weakening of autonomic nerve control can reduce the complexity of the heartbeat signal. Estimate the separation performance of all features, the best discrimination is obtained for the area under the mass index spectrum S1τ as providing 100% accuracy in separating the Healthy Young and CHF groups, and 90.93% separation accuracy between the Healthy Elderly and CHF groups. This work provide a good basis for the diagnosis of CHF with a novel perspective.

Keywords

• Congestive heart failure
• heartbeat time series
• multifractal detrended fluctuation analysis