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In this paper, nonlinear dynamical analysis based on recurrence quantification analysis (RQA) has been used for characterizing the nonlinear EEG dynamics. RQA can provide some useful quantitative information on the regular, chaotic or stochastic nature of the underlying dynamics. We use the RQA-based measures as the quantitative features of the nonlinear EEG dynamics. Mutual information (MI) is used to find the most relevant feature subset from RQA-based features. The selected features are fed into an artificial neural network for classification of EEG recordings to detect ictal, interictal, and healthy states. The performance of the proposed method is tested using a publicly available benchmark database for various classification cases. The combination of five selected features based on MI achieved 100% accuracy, which demonstrates the superiority of the proposed method for seizure detection.

نوع مطالعه: Original | موضوع مقاله: Computational Neuroscience
دریافت: ۱۳۹۵/۱۲/۱۳ | پذیرش: ۱۳۹۶/۷/۱۲ | انتشار: ۱۳۹۶/۱۲/۱۳

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