Softmax函数
计算机科学
人工智能
癫痫发作
特征提取
卷积神经网络
模式识别(心理学)
判别式
脑电图
深度学习
加权
人工神经网络
癫痫
支持向量机
感知器
特征(语言学)
精神科
神经科学
哲学
放射科
生物
医学
语言学
心理学
作者
Jiuwen Cao,Jiahua Zhu,Wenbin Hu,Anton Kummert
出处
期刊:IEEE Transactions on Cognitive and Developmental Systems
[Institute of Electrical and Electronics Engineers]
日期:2020-12-01
卷期号:12 (4): 709-722
被引量:57
标识
DOI:10.1109/tcds.2019.2936441
摘要
The scalp electroencephalogram (EEG)-based epileptic seizure/nonseizure detection has been comprehensively studied, and fruitful achievements have been reported in the past. Yet, few investigations have been paid to the preictal stage detection, which is practically more crucial to epileptics in taking precautions before seizure onset. In this article, a novel epileptic preictal state classification and seizure detection algorithm based on deep features learned by stacked convolutional neural networks (SCNNs) is developed. The mean amplitude of sub-band spectrum map (MAS) obtained from the average sub-band spectra of multichannel EEGs is adopted for representation. The probability feature vectors by stacked convolutional neural networks (CNNs) are extracted in the softmax layer of CNNs, where an adaptive and discriminative feature weighting fusion (AWF) is developed for performance enhancement. Following the deep extraction layer, the effective kernel extreme learning machine (KELM) is adopted for feature learning and epileptic classification. Experiments on the benchmark CHB-MIT database and a real recorded epileptic database are conducted for performance demonstration. Comparisons to many state-of-the-art epileptic classification methods are provided to show the superiority of the proposed SCNN+AWF algorithm.
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