Wajid Aziz

Inverse Correlation between Heart Rate Variability and Heart Rate Demonstrated by Linear and Nonlinear Analysis.

The dynamical fluctuations in the rhythms of biological systems provide valuable information about the underlying functioning of these systems. During the past few decades analysis of cardiac function based on the heart rate variability (HRV; variation in R wave to R wave intervals) has attracted great attention, resulting in more than 17000-publications (PubMed list). However, it is still controversial about the underling mechanisms of HRV. In this study, we performed both linear (time domain and frequency domain) and nonlinear analysis of HRV data acquired from humans and animals to identify the relationship between HRV and heart rate (HR). The HRV data consists of the following groups: (a) human normal sinus rhythm (n = 72); (b) human congestive heart failure (n = 44); (c) rabbit sinoatrial node cells (SANC; n = 67); (d) conscious rat (n = 11). In both human and animal data at variant pathological conditions, both linear and nonlinear analysis techniques showed an inverse correlation between HRV and HR, supporting the concept that HRV is dependent on HR, and therefore, HRV cannot be used in an ordinary manner to analyse autonomic nerve activity of a heart.

Time series analysis and risk assessment of domestic radon: Data collected in dwellings along fault lines

Indoor radon-in-air (IR) concentrations in dwellings situated on a fault line passing underneath Muzaffarabad City were measured with a continuous radon monitor. Measurements were taken at half an hour intervals at 11 sites for 48 h. The values ranged from 14 ± 1 to 155 ± 4 Bq.m− 3 with an average value of 42 ± 2 Bq.m−3. The geometric mean (GM), standard deviation (STD) and geometric standard deviation (GSD) were 32 ± 2, 40.2 ± 0.8 and 1.5 Bq.m−3, respectively. Annual mean effective doses ranged between 0.35 ± 0.03 and 3.9 ± 0.1 mSv.y−1 with an overall mean value 1.05 ± 0.05 mSv.y−1. The experimental data were analysed by comparing algorithms to extract phase space dynamical information in the radon time series. Such approaches are potentially useful for deriving methods of extrapolation of short-term measurements to annualized average predictions and for earthquake detection. The computed values for Hurst exponent (H) from the rescaled range analysis and the Lyapunov exponent (L) provided estimates of the degree of chaotic regime. The Hurst exponent (0.877) for the radon time series indicates that the radon time series is a persistent time series. The value of Lyapunov exponent (0.0021) shows deterministic chaos in the data. The permutation entropy value was 0.6894 reflecting the irregularity in IR time series. The value of entropy obtained confirmed that the predictability of the IR time series is restricted to approximately one time step into the future. The mean value of IR concentrations corresponds to excess relative risks (ERR) of radon-induced lung cancer for 35- and 55-year-olds at all sites of 0.25 ± 0.01 and 0.205 ± 0.01, respectively. The observed 48-h average level (42 ± 2 Bq.m−3) was less than those recommended by the World Health Organization (WHO) (100 Bq.m−3), US Environmental Protection Agency (148 Bq.m−3) and Health Protection Agency, UK (200 Bq.m−3).

Comparative study of multiscale entropy analysis and symbolic time series analysis when applied to human gait dynamics

The chronological vacillations in the stride to stride interval provide a noninvasive method to assess the influence of malfunction of human gait and its alterations with disease and age. To extract information from the human stride interval, various complexity analysis techniques have been proposed. In the present study, the comparison of two recently developed complexity analysis methodologies: multiscale entropy (MSE) and symbolic entropy (SyEn) has been made. These techniques were applied to stride interval time series data of human gait walking at normal and metronomically paced stressed conditions. Wilcoxon-rank-sum test (Mann-Whitney-Wilcoxon (MWW) test) was used to find the significant difference between the groups. For each method of analysis, parameters were adjusted to optimize the separation of the groups. The symbolic entropy method provided maximum separation at wide range of threshold values and this measure was found to be more robust for analyzing the human gait data as compared to multiscale entropy in the presence of dynamical and observational noise. The results of this study can have implication modeling physiological control mechanism and for quantifying human gait dynamics in physiological and stressed conditions.

Numerical simulations of natural convection heat transfer along a vertical cylinder

Purpose – The purpose of this paper is to present a numerical solution for the problem of steady laminar flow and heat transfer characteristics of viscous incompressible fluid.Design/methodology/approach – For this purpose a two dimensional code has been developed to simulate the natural convection heat transfer along a vertical cylinder, for four different geometries: from vertical cylinder in infinite medium; from a vertical flat plate in an infinite medium; from an open assembly of a finite vertical cylinder; and from an open rectangular pitch assembly of cylinders.Findings – The effects of various parameters of interest have been discussed through simulations. The Nusselt numbers of constant wall temperature and constant heat flux cylinders calculated numerically and compared with Lee et al. and Heckel et al., respectively, and are found within reasonable agreement. For large radius, a vertical cylinder has been treated as a vertical flat plate, so that the curvature effects become negligible. For the...

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.