脑电图
人工智能
模式识别(心理学)
计算机科学
灵敏度(控制系统)
小波
贝叶斯概率
主成分分析
线性判别分析
朴素贝叶斯分类器
语音识别
癫痫
支持向量机
心理学
神经科学
工程类
电子工程
作者
Dongling Ma,Shasha Yuan,Junliang Shang,Jin-Xing Liu,Lingyun Dai,Xiang-Zhen Kong,Fangzhou Xu
标识
DOI:10.1142/s0129065721500064
摘要
Electroencephalogram (EEG) plays an important role in recording brain activity to diagnose epilepsy. However, it is not only laborious, but also not very cost effective for medical experts to manually identify the features on EEG. Therefore, automatic seizure detection in accordance with the EEG recordings is significant for the diagnosis and treatment of epilepsy. Here, a new method for detecting seizures using tensor distance (TD) is proposed. First, the time–frequency characteristics of EEG signals are obtained by wavelet transformation, and the tensor representation of EEG signals is then obtained. Tucker decomposition is used to obtain the principal components of the EEG tensor. After, the distances between different categories of EEG tensors are calculated as the EEG features. Finally, the TD features are classified through the Bayesian Linear Discriminant Analysis (Bayesian LDA) classifier. The performance of this method is measured by the sensitivity, specificity, and recognition accuracy. Results indicate 95.12% sensitivity, 97.60% specificity, 97.60% recognition accuracy, and a false detection rate of 0.76 per hour in the invasive EEG dataset, which included 566.57[Formula: see text]h of EEG recording data from 21 patients. Taken together, the results show that TD has a good detection effect for seizure classification and that this method has high computational speed and great potential for real-time diagnosis.
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