Non-linear Methods in HRV Analysis

Heart rate variability (HRV) analysis has become an important tool in cardiology, these noninvasive measurements are relatively easy to perform, have good reproducibility and also provide prognostic information on patients with cardiac diseases. There are various methods in use to analyze the HRV; these methods usually can help in the early detection of some cardiac diseases. HRV analysis (meaning the study of hearts inter-beat time intervals) is useful for understanding the status of the Autonomic Nervous System (ANS). HRV reflects the cardiac system's ability to adapt to the changing external or internal circumstances by detecting and fast responding to the unexpected and unpredictable stimuli. HRV analysis has the ability to assess overall cardiac health and the state of the ANS responsible for regulating cardiac activity. This paper presents a detrended fluctuation analysis of RR time intervals and of their discrete wavelet transforms, comparing longer and shorter time series in order to find long term significant variations in the studied signals. Signals are taken from MIT-BIH Long Term ECG database, the analysis is performed under MATLAB environment.