Heart rate variability analysis using approximate entropy and detrended fluctuation for monitoring heart condition

Variation in time between two successive heart beats occurring due to internal and external stimulation causes Heart Rate Variability (HRV). HRV is a tool for indirect investigation of both cardiac and autonomic system function in both healthy and diseased condition. It has been speculated that HRV analysis by nonlinear method might bring potentially useful prognosis information into light which will be helpful for assessment of cardiac condition. In this study, HRV from two types of data sets (normal sinus rhythm and sinus arrhythmia) are analyzed which are stored in MIT-BIH (Massachusetts Institute of Technology - Beth Israel hospital) database, an extended collection of recorded physiological signals. Then two nonlinear methods, approximate entropy (ApEn) and detrended fluctuation analysis (DFA), have been applied to analyze HRV of both Arrhythmia patients and people having normal sinus rhythm. It has been clearly shown that nonlinear parameters obtained from these two methods reflect the opposite heart condition of the two types of subjects under study, healthy and diseased, by HRV measures. Thus, value of the nonlinear parameters found in this work can be used as standard when treating suspected patients for diagnosis of Arrhythmia. Also, by measuring these nonlinear parameter values, heart condition can be understood.