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Issue title: Impact of advanced parallel or cloud computing technologies for image guided diagnosis and therapy
Guest editors: Kelvin K.L. Wong, Simon Fong and Defeng Wang
Article type: Research Article
Authors: Zhang, Zhongnan* | Wen, Tingxi | Huang, Wei | Wang, Meihong | Li, Chunfeng
Affiliations: Software School, Xiamen University, Xiamen, Fujian, China
Correspondence: [*] Corresponding author: Zhongnan Zhang, Software School, Xiamen University, Xiamen, Fujian 361005, China. Tel.: +86 13720875570; Fax: +86 592 2580500; E-mail: zhongnan_zhang@xmu.edu.cn.
Abstract: BACKGROUND: Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. OBJECTIVE: In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). METHODS: New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. RESULTS: Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. CONCLUSIONS: MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.
Keywords: EEG, SVM, genetic algorithm, neurological diseases, multi-fractal detrended fluctuation analysis, cloud computing
DOI: 10.3233/XST-17258
Journal: Journal of X-Ray Science and Technology, vol. 25, no. 2, pp. 261-272, 2017
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