Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Marjan Mozaffarilegha; Seyed Mohammad Sadegh Movahed
Volume 11, Issue 3 , September 2017, , Pages 255-264
Abstract
The complexities and the effects of inter-subject variations on the encoding of sounds are features of the brainstem processing. Examining such data based on linear analysis is not reliable, encouraging to take into account non-linear methods which are effective ways of explaining such non-stationary ...
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The complexities and the effects of inter-subject variations on the encoding of sounds are features of the brainstem processing. Examining such data based on linear analysis is not reliable, encouraging to take into account non-linear methods which are effective ways of explaining such non-stationary signals. The purpose of this study is to explore the behavior of the brainstem in response to complex auditory stimuli /da/ using Multifractal Detrended Fluctuation Analysis modified by Singular Value Decomposition (SVD), Adaptive Detrending (AD) and Empirical Mode Decomposition (EMD). Auditory brainstem responses to synthetic /da/ stimuli were recorded for 40 normal subjects with a mean age of 22.7 years. MFDFA is carried out on the s-ABR time series data to evaluate the variation of their complexity and multiscaling. To utilize optimal Detrending of s-ABR time series, AD, SVD and EMD algorithms are applied on time series. By computing the fluctuation function and evaluating scaling behavior, scaling exponents such as generalized Hurst exponent and multifractal spectrum are determined. Given results in this method indicate that underlying signal has non-stationary nature in small scales, but property of system is controlled by trend in large scales. There is a crossover at msec on the behavior of fluctuation function corresponding to dominant sinusoidal trend in all samples. The average of Hurst exponent is at 68% confidence interval in small scales msec. The -dependency of demonstrate that underlying data sets have multifractality nature and are almost due to long-range correlations. The width of singularity spectrum which is a measure of the signal complexity of underlying data in average equates to at confidence interval.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mina Hemmatian; Ali Maleki
Volume 9, Issue 2 , July 2015, , Pages 163-178
Abstract
The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion ...
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The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion to evaluate chaotic systems and to quantify the fractal dimensions. Previous studies have shown that classical Hurst index is not appropriate for classification of cardiac arrhythmias because not only selection of algorithm parameters affect the value of determined Hurst index, but also it significantly varies as the heart rate changes. In this paper, modified multiple Hurst index has been proposed to classify the cardiac arrhythmias. The presented index is resistant against changes in heart rate and can be used to identify appropriate features to classify the cardiac arrhythmias. 80 signal from four types of ECG beats obtained from the MIT-BIH Arrhythmia dataset has been used to validate the algorithm. Results show that this method is able to detect normal rhythm and right bundle branch block (RBBB), left bundle branch block (LBBB) and atrial premature complex (APC) arrhythmias with accuracy of 100%, 96.25% and 88.75% using artificialneural network, k nearest neighbor and LDA classifiers respectively.
