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Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity

1
Key Laboratory of Shenzhen Internet of Things Terminal Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
2
School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(11), 598; https://doi.org/10.3390/e19110598
Received: 27 September 2017 / Revised: 31 October 2017 / Accepted: 3 November 2017 / Published: 8 November 2017
(This article belongs to the Section Complexity)
In the diagnosis of neurological diseases and assessment of brain function, entropy measures for quantifying electroencephalogram (EEG) signals are attracting ever-increasing attention worldwide. However, some entropy measures, such as approximate entropy (ApEn), sample entropy (SpEn), multiscale entropy and so on, imply high computational costs because their computations are based on hundreds of data points. In this paper, we propose an effective and practical method to accelerate the computation of these entropy measures by exploiting vectors with dissimilarity (VDS). By means of the VDS decision, distance calculations of most dissimilar vectors can be avoided during computation. The experimental results show that, compared with the conventional method, the proposed VDS method enables a reduction of the average computation time of SpEn in random signals and EEG signals by 78.5% and 78.9%, respectively. The computation times are consistently reduced by about 80.1~82.8% for five kinds of EEG signals of different lengths. The experiments further demonstrate the use of the VDS method not only to accelerate the computation of SpEn in electromyography and electrocardiogram signals but also to accelerate the computations of time-shift multiscale entropy and ApEn in EEG signals. All results indicate that the VDS method is a powerful strategy for accelerating the computation of entropy measures and has promising application potential in the field of biomedical informatics. View Full-Text
Keywords: entropy; approximate entropy; sample entropy; multiscale entropy; EEG; time-shift multiscale entropy entropy; approximate entropy; sample entropy; multiscale entropy; EEG; time-shift multiscale entropy
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Lu, Y.; Wang, M.; Peng, R.; Zhang, Q. Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity. Entropy 2017, 19, 598.

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