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Algorithm for Detection of Convulsive Seizures in Severe Encephalitis Based on Multiscale Compressed Shapelets

Journal of the American Society of Echocardiography(2024)SCI 2区SCI 3区

Hangzhou Dianzi Univ

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Abstract
Encephalitis is a serious disease for neurological dysfunction caused by inflammation of the brain parenchyma. Recurrent convulsive seizures or nonconvulsive status are the main causes of many neurological sequelae. Accurately identifying convulsive seizure signals in continuous electroencephalogram (EEG) signals collected from patients with severe encephalitis can help doctors effectively make diagnoses and give treatment plans. Traditionally, fourfold-scale compressed amplitude-integrated electroencephalography (aEEG) is used for detection. In this article, a novel convulsive seizure detection method of encephalitis based on multiscale aEEG signal is proposed. First, continuous EEG (cEEG) signal is converted into multiscale aEEG. Second, multiscale shapelets from multiscale aEEG signals are extracted to form a multiscale ictal waveform codebook. Third, the dynamic time warping (DTW) algorithm is used to calculate the similarity between the codebook and the actual waveform. Finally, random forest (RF) classifier is applied to train and test the method. In the dataset collected, the accuracy, sensitivity based on the event, specificity, F1 score, mean absolute error, missed detection, false detection, and false positive ratio based on the event obtained by the proposed method reach 96.32%, 95.70%, 97.22%, 96.85%, 0.037, 4.71%, 3.70%, and 0.06 times/h, respectively.
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Key words
Electroencephalography,Pediatrics,Time series analysis,Electrodes,Accuracy,Recording,Machine learning,Feature extraction,Data mining,Radio frequency,Amplitude-integrated electroencephalography (aEEG),convulsive seizure,multiscale shapelets,multisensor electroencephalogram (EEG),severe encephalitis
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要点】:本文提出了一种基于多尺度压缩形状let的脑炎患者癫痫发作检测算法,提高了对脑电图信号的识别准确性和效率。

方法】:该方法首先将连续脑电图信号转换为多尺度振幅整合脑电图,然后从多尺度振幅整合脑电图信号中提取多尺度形状let,构建多尺度发作波形码本,利用动态时间扭曲算法计算码本与实际波形之间的相似度,最后通过随机森林分类器进行训练和测试。

实验】:在收集的数据集上,所提方法实现了96.32%的准确率、95.70%的事件敏感性、97.22%的特异性、96.85%的F1分数、0.037的平均绝对误差、4.71%的漏检率、3.70%的误检率和0.06次/小时的事件基础上的假阳性比率。