Analysis of Linear and Non-linear parameters of HRV for opting optimum parameters in wearable device

Abstract

The Human heart rate though appears regular, it is not. There is a change in the time intervals between two successive heart beats, these variations are referred to as heart rate variability (HRV). The HRV will be in certain regular pattern and differs from individuals. HRV depends on state of pathology and physiology of the individual. Research has been carried out to find whether if they could predict the disease early and could avoid complications which might be fatal. It helps the physician in early diagnosis and alertness of the disease which will denote the step for better care. The vital advantage of HRV is that, it is noninvasive and the individuals can check there HRV parameters without any inconvenience at any time without any after effects. In this work ECG signals are obtained from physionet data base. Data of normal ECG signals and ECG of Supraventricular Tachycardia of 57 subjects each are considered for the study of HRV parameters. Linear parameters of HRV like time-domain, frequency-domain parameters non-linear parameters like Poincare plot is analyzed. The new software is developed on MATLAB tool for carrying HRV analysis, code has been developed to obtain linear and nonlinear parameters of HRV with the filtering option embedded in the software. Using the developed software statistical analyses was carried on to obtain the optimum parameter that can distinguish normal and diseased conditions which can be further considered for embedding in wearable device hardware. Though HRV has huge potential in diagnosing and predicting diseases, it is yet to be used as a diagnostic tool. Identification of Linear and non-linear parameters which are significant in diagnoses of diseases will be useful in predicting ANS modifying diseases early.