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The Automatic Detection of Seizure Based on Tensor Distance And Bayesian Linear Discriminant Analysis
International Journal of Neural Systems ( IF 8 ) Pub Date : 2021-01-30 , DOI: 10.1142/s0129065721500064
Delu Ma 1 , Shasha Yuan 1 , Junliang Shang 1 , Jinxing Liu 1 , Lingyun Dai 1 , Xiangzhen Kong 1 , Fangzhou Xu 2
Affiliation  

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 566.57h 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.

中文翻译:

基于张量距离和贝叶斯线性判别分析的癫痫发作自动检测

脑电图 (EEG) 在记录大脑活动以诊断癫痫方面起着重要作用。然而,医学专家手动识别EEG上的特征不仅费力,而且成本效益不高。因此,根据脑电图记录自动检测癫痫发作对于癫痫的诊断和治疗具有重要意义。在这里,提出了一种使用张量距离 (TD) 检测癫痫发作的新方法。首先,通过小波变换得到脑电信号的时频特征,进而得到脑电信号的张量表示。Tucker分解用于获得EEG张量的主成分。之后,计算不同类别的脑电图张量之间的距离作为脑电图特征。最后,TD特征通过贝叶斯线性判别分析(贝叶斯LDA)分类器进行分类。该方法的性能通过灵敏度、特异性和识别准确度来衡量。结果表明,侵入性脑电图数据集的敏感性为 95.12%,特异性为 97.60%,识别准确率为 97.60%,误检率为 0.76/小时,其中包括 566.57h 的脑电图记录来自 21 名患者的数据。综上所述,结果表明,TD对癫痫发作分类具有良好的检测效果,该方法计算速度快,实时诊断潜力巨大。
更新日期:2021-01-30
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