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Wajid Aziz

Bio: Wajid Aziz is an academic researcher from University of Azad Jammu and Kashmir. The author has contributed to research in topics: Electroencephalography & Sample entropy. The author has an hindex of 14, co-authored 59 publications receiving 781 citations. Previous affiliations of Wajid Aziz include University of Kashmir & IT University.


Papers
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Proceedings ArticleDOI
01 Dec 2005
TL;DR: In this article, a modified procedure called multiscale permutation entropy (MPE) was proposed to quantify the complexity of time series in the presence of dynamical and observational noise.
Abstract: Time series derived from simpler systems are single scale based and thus can be quantified by using traditional measures of entropy. However, times series derived from physical and biological systems are complex and show structures on multiple spatio-temporal scales. Traditional approaches of entropy based complexity measures fail to account for multiple scales inherent in these time series. Recently multi-scale entropy (MSE) method was introduced, which provide a way to measure complexity over a range of scales. MSE method uses sample entropy, a refinement of approximate entropy to quantify the complexity of time series. Nonstationarity, outliers and artifacts affect the sample entropy values because they change time series standard deviation and therefore, the value of similarity criterion. In this paper, we have used permutation entropy for quantifying the complexity, which is useful in the presence of dynamical and observational noise. We called this modified procedure multiscale permutation entropy (MPE). We observed that MPE is robust in presence of artifacts and robustly separates pathological and healthy groups

153 citations

Journal ArticleDOI
TL;DR: In the study, it is found that the complexity of physiological signal was higher than that of random signals at short threshold values and this measure of complexity showed significant difference between control and neurodegenerative disease subjects for a certain range of thresholds.
Abstract: The stride interval of human gait fluctuates in complex fashion. It reflects the rhythm of the locomotor system. The temporal fluctuations in the stride interval provide us a non-invasive technique to evaluate the effects of neurological impairments on gait and its changes with age and disease. In this paper, we have used threshold dependent symbolic entropy, which is based on symbolic nonlinear time series analysis to study complexity of gait of control and neurodegenerative disease subjects. Symbolic entropy characterizes quantitatively the complexity even in time series having relatively few data points. We have calculated normalized corrected Shannon entropy (NCSE) of symbolic sequences extracted from stride interval time series. This measure of complexity showed significant difference between control and neurodegenerative disease subjects for a certain range of thresholds. We have also investigated complexity of physiological signal and randomized noisy data. In the study, we have found that the complexity of physiological signal was higher than that of random signals at short threshold values.

87 citations

Journal ArticleDOI
TL;DR: In this paper, the results of ambient outdoor gamma dose rates measured for Jhelum valley of the state of Azad Kashmir were presented by using Ludlum micrometer-19 which is an active and portable detector.

76 citations

Journal ArticleDOI
23 Jun 2016-PLOS ONE
TL;DR: 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.
Abstract: 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.

62 citations

Journal ArticleDOI
TL;DR: The HRV parameters showed no significant difference between normal and IUGR children and the effect of gender on HRV measures was also examined and it was noticed that girls had lower HRV than boys.
Abstract: Intrauterine growth restriction (IUGR) has been associated with an increased risk of cardiovascular disease in later life. The regularity mechanism of cardiovascular system is under the control of autonomic nervous system (ANS). The non-optimal fetal growth may alter the development of the ANS and this appears to persist in later life. The aim of the present work is to analyse the synergic activity of the ANS in normal and growth restricted children. Heart rate variability analysis from 24 h ECG recordings of 70 children between 9 and 10 years old, normal and IUGR was performed using linear and non-linear time series analysis techniques. The HRV parameters showed no significant difference between normal and IUGR children. Low birth weight and its association with development of the cardiovascular system and its control have been extensively studied. In order to investigate the effect of low birth weight on HRV parameters, the IUGR children were further divided into two groups: IUGR-1 (birth weight <2.50 kg) and IUGR-2 (birth weight ≥2.50 kg). The results demonstrated that most of the HRV measures showed significant differences between normal versus IUGR-1 as well as IUGR-1 versus IUGR-2 groups. The effect of gender on HRV measures was also examined and we noticed that girls had lower HRV than boys.

55 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: Although women showed greater mean heart rate, they showed greater vagal activity indexed by HF power of HRV, and underlying mechanisms of these findings are discussed.

458 citations

Proceedings Article
01 Jan 2011
TL;DR: This work introduces the first complexity-invariant distance measure for time series, and shows that it generally produces significant improvements in classification accuracy, and it is shown that this improvement does not compromise efficiency, since it can be lower bound and use a modification of triangular inequality.
Abstract: The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While there is a plethora of classification algorithms that can be applied to time series, all of the current empirical evidence suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping. In this work we make a surprising claim. There is an invariance that the community has missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where complex objects are incorrectly assigned to a simpler class. We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series classification experiments ever attempted, and show that complexity-invariant distance measures can produce improvements in accuracy in the vast majority of cases.

310 citations