计算机科学
人工智能
模式识别(心理学)
脑电图
卷积神经网络
癫痫
支持向量机
深度学习
人工神经网络
机器学习
卷积(计算机科学)
作者
Qi Xin,Shaohai Hu,Shuaiqi Liu,Xiaole Ma,Xumu Zhang,Yudong Zhang
出处
期刊:Journal of Medical Imaging and Health Informatics
[American Scientific Publishers]
日期:2021-01-01
卷期号:11 (1): 25-32
被引量:6
标识
DOI:10.1166/jmihi.2021.3259
摘要
Clinical Electroencephalogram (EEG) data is of great significance to realize automatable detection, recognition and diagnosis to reduce the valuable diagnosis time. To make a classification of epilepsy, we constructed convolution support vector machine (CSVM) by integrating the advantages of convolutional neural networks (CNN) and support vector machine (SVM). To distinguish the focal and non-focal epilepsy EEG signals, we firstly reduced the dimensionality of EEG signals by using principal component analysis (PCA). After that, we classified the epilepsy EEG signals by the CSVM. The accuracy, sensitivity and specificity of our method reach up to 99.56%, 99.72% and 99.52% respectively, which are competitive than the widely acceptable algorithms. The proposed automatic end to end epilepsy EEG signals classification algorithm provides a better reference for clinical epilepsy diagnosis.
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