Abstract—Non linear methods of heart rate variability (HRV) analysis are becoming more popular. It has been observed that complexity measures quantify the regularity and uncertainty of cardiovascular RR-interval time series. In the present work, SampEn has been evaluated in healthy normal sinus rhythm (NSR) male and female subjects for different data lengths and tolerance level r. It is demonstrated that SampEn is small for higher values of tolerance r. Also SampEn value of healthy female group is higher than that of healthy male group for short data length and with increase in data length both groups overlap each other and it is difficult to distinguish them. The SampEn gives inaccurate results by assigning higher value to female group, because male subject have more complex HRV pattern than that of female subjects. Therefore, this traditional algorithm exhibits higher complexity for healthy female subjects than for healthy male subjects, which is misleading observation. This may be due to the fact that SampEn do not account for multiple time scales inherent in the physiologic time series and the hidden spatial and temporal fluctuations remains unexplored. Keywords—Heart rate variability, normal sinus rhythm group, RR interval time series, sample entropy. I. INTRODUCTION N recent years, HRV has emerged as a powerful non- invasive diagnostic tool used to investigate the autonomic control on the cardiac activity. HRV is the variation in beat-to- beat intervals and is one of the most important markers for evaluating overall cardiac health. It is a proven fact that HRV is usually high in normal and healthy subjects, whereas reduced HRV has been observed in certain pathologies such as myocardial infarction, ischemic heart disease, congestive heart failure and others [1]-[5]. Time and frequency domains are the linear methods, used to access the autonomic nervous system control over cardiac rhythm. These Linear methods of HRV analysis assume that R-R interval series to be stationary or any variations in it are harmonic or sinusoidal in nature. But cardiac rhythm has multiple interactions with other physiological systems such as respiration and it may also be affected by small disturbances such as premature ventricular contraction, atrioventricular block etc. so resulting signal is nonlinear, non-stationary and chaotic in nature, exhibiting some short range and long range correlations [6]. Linear approach is more prone to give Ramesh K. Sunkaria is with the Department of Electronics and Communication Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab)-144011, India (phone no. 0181-2690301; e- mail: sunkariark@gmail.com). Puneeta with the Department of Electronics and Communication Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab)-144011, India (e-mail: puneetamarwaha@gmail.com). inaccurate analysis. Also the sensitivity and specificity of these methods was less than expected positive predictive value of < 30% [7]-[9]. The highly complex heart rate signals are nonlinear and non- stationary, usually chaotic in nature and exhibits some short range and long range correlations. Analysis of HRV signal using non linear approach is proposed to give better results, because here original RR time series is analyzed. With the help of these nonlinear methods one can analyze the hidden complexity of RR interval series. Also the predictive value is expected to be higher than the linear methods [10]. Some of the nonlinear dynamics used are the poincare plot, correlation dimension, power law slope, largest lyupnov exponent, and approximate entropy (ApEn), sample entropy (SampEn) and detrended fluctuation analysis. Entropy based measures are used to quantify the regularity and uncertainty of cardiovascular RR interval time series. Pincus [11]-[13] introduced ApEn for measuring complexity of a time series. But ApEn statistics gives inconsistent results and is biased suggesting less complexity than actually present in the signal. Richman and Mooran [14] developed new refined complexity measure SampEn, which agree with the theory much more closely than ApEn. Tuzcu and Selman [15] observed significant decrease in SampEn in children who undergone heart transplant, thereby indicating loss of system complexity. Al-Angari and Sahakian [16] reported significant loss of complexity in patients suffering from obstructive sleep apnea syndrome. Loss of complexity has been proposed with age [17] and in certain pathological conditions [18]. Goya- Esteban et al. [18] applied SampEn to distinguish healthy subjects from patients with congestive heart failure at fixed tolerance level r. Also it has been observed that HRV of male subjects is higher in comparison to female subjects [22]-[25]. In this paper we evaluated SampEn technique on NSR male and female subjects to observe the effect of the variation in data length N and tolerance level r on the SampEn. II. MATERIALS AND METHOD The entire dataset is retrieved from the physionet site (http://www.physionet.org/) [19], consists of RR time series recorded from 10 healthy subjects, 5 male and 5 female subjects, each sampled at 128 samples per second (from MIT- BIH NSR Database) All the database of RR interval time series is filtered to remove outliers [20]. Table I presents the record numbers of these subjects. Ramesh K. Sunkaria, Puneeta Marwaha Gender Based Variability Time Series Complexity Analysis I World Academy of Science, Engineering and Technology International Journal of Biomedical and Biological Engineering Vol:8, No:3, 2014 129 International Scholarly and Scientific Research & Innovation 8(3) 2014 ISNI:0000000091950263 Open Science Index, Biomedical and Biological Engineering Vol:8, No:3, 2014 publications.waset.org/9997627/pdf