A. Q. Abbasi, and W. A. Loun. Symbolic Time Series Analysis of Temporal Gait Dynamics. In Journal of Signal Processing Systems, vol. 74(3) , 2014
Signals obtained from biological systems exhibit pronounced complexity. The patterns of change contain valu- able information about the dynamics of underlying control mechanism of the complex biological systems. Human gait is a complex process with multiple inputs and numerous outputs. Various complexity analysis tools have been proposed to extract information from human gait time series. In this study, we used recently developed threshold based symbolic entropy to compare the spontaneous output of the human locomotors system during constrained and metronomically paced walking protocols. For that purpose, stride interval time series of healthy subjects who walked for 1 h at normal, slow and fast rates under different conditions was transformed into symbol sequences. Normalized corrected Shannon entropy (NCSE) was computed from the symbol sequences of the stride inter- val time series. The findings indicated that the unprompted output of human locomotors system is more complex during unconstrained normal walking as compared with slow, fast or metronomically paced walking
Conference Contributions (1)
L. Hussain, W. Aziz, S. A. Nadeem, and A. Q. Abbasi. Classification of Normal and Pathological Heart Signal Variability Using Machine Learning Techniques. In International Journal of Darshan Institute on Engineering Research and Emerging Technology, vol. 3(2) , pp. 13-19, 2014
The guide Heart rate signals provide valuable information for assessing the state of autonomic nervous system that control functioning of heart. Heart rate variability analysis is an important non-invasive tool that has been widely used for assessing autonomic control of heart using linear and non-linear techniques since last three decades. Different methods used to detect these beats include ECG, blood pressure etc. but ECG has great importance because it gives a complete and clear waveform. Heart rate variability analysis is a tool that assesses the autonomic nervous system. It is based on the measurement of changing heart rate signals. In past two decades a large number of research efforts were made and a number of techniques were proposed for heart rate variability. In this study, the techniques used for HRV analysis includes linear (time and frequency domain) and non-linear techniques. We have used different classifiers and their methods to check heart rate variations in healthy cases and diseased cases. Methods showing highest accuracy include Naïve Bayes method of Bayes classifier; sequential minimal optimization (SMO) of functions classifier, lazy locally weighted learning (LWL) method, AdaBoost and logical model tree. Among all these methods LMT (logical model tree) is considered as best method with the accuracy level of 92.5%. In this study 10 folds cross-validation was used as test option. Cross-validation is a technique to assess the accuracy of results where the goal is predicted. In 10 folds cross-validation 10 times repetition occurs and the result is obtained by taking mean accuracy.