模式识别(心理学)
计算机科学
脑电图
人工智能
癫痫发作
特征选择
相互信息
感知器
癫痫
人工神经网络
希尔伯特-黄变换
多层感知器
特征(语言学)
分类器(UML)
神经科学
心理学
滤波器(信号处理)
哲学
语言学
计算机视觉
作者
Kazi Mahmudul Hassan,Rabiul Islam,Thanh Nguyen,Md. Khademul Islam Molla
标识
DOI:10.1016/j.eswa.2021.116414
摘要
Epilepsy is a group of neurological disorders that affect normal brain activities and human behavior. Electroencephalogram based automatic epileptic seizure detection has significant applications in epilepsy treatment and medical diagnosis. In this study, a novel epileptic seizure detection method is proposed with a combination of empirical mode decomposition, mutual information-based best individual feature (MIBIF) selection algorithm and multi-layer perceptron neural network. Initially, fixed length EEG epochs are decomposed into amplitude and frequency-modulated components called intrinsic mode functions (IMFs). Three features named ellipse area of second-order difference plot, variance and fluctuation index are calculated from first few IMFs. The most significant features are then selected from the calculated features using the MIBIF algorithm to produce a final feature set. Later, the generated feature set is fed into the multi-layer perceptron neural network (MLPNN) classifier. Two well-known benchmark epileptic EEG datasets are used in this study for experimental evaluations. The result of proposed approach shows a significant performance improvement compared to the recent state-of-the-art methods.
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