Multi-fractal detrended fluctuation half-spectrum analysis of HRV

• Main Authors: Daoming Zhang, Cong Wang, Chuangye Li, Wei Dai
• Format: Article
• Language: English
• Published: Wiley 2019-11-01
• Series: The Journal of Engineering
• Subjects: fractals; fluctuations; medical signal processing; electrocardiography; diseases; statistical distributions; signal classification; ecg signals; heart diseases; multifractal detrended fluctuation half-spectrum analysis method; short-term heart rate variability; normal heart rate; sporadic heart rate variation; box plot analysis; classification accuracy; disease condition; statistical value distribution; nonlinear dynamics; heartbeat process; electrocardiogram; cardiac electrical activity; multifractal parameter; heart rate mutation signal; supraventricular rate
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1067

Electrocardiogram is a comprehensive reflection of cardiac electrical activity on the surface of the body. It is of great research value to analyse ECG signals and diagnose heart diseases. Since the heartbeat process is a chaotic system, this study proposes a multi-fractal detrended fluctuation half-spectrum analysis method of short-term heart rate variability-y (HRV) signals from the perspective of non-linear dynamics, for patients with normal heart rate, sporadic heart rate variation and supraventricular rate, the heart rate mutation signal of patients with heart rate variation is calculated, and the statistical value distribution of multi-fractal parameter α has a certain change trend with the change of disease condition. The classification accuracy can reach >75% through box plot analysis.