School of Information Science and Engineering, Shandong University, PR China.
School of Information Science and Engineering, Shandong University, PR China.
Epilepsy Behav. 2014 Feb;31:339-45. doi: 10.1016/j.yebeh.2013.10.005. Epub 2013 Nov 20.
Approximately 1% of the world's population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.
大约有 1%的世界人口患有癫痫。自动癫痫检测系统对于癫痫的监测和诊断具有重要意义。本文提出了一种新的方法，用于颅内 EEG 记录中的自动癫痫检测。将 EEG 记录分成 4 秒的时间段，然后对 EEG 时间段进行五尺度的小波分解。选择尺度 3、4 和 5 的细节信号来形成信号分布。提取扩散距离作为特征，并使用贝叶斯线性判别分析 (BLDA) 作为分类器。该系统共使用了 21 名患者的 193.75 小时颅内 EEG 记录，记录了 87 次发作，平均灵敏度为 94.99%，特异性为 98.74%，假阳性率为 0.24/h。基于扩散距离的癫痫检测系统在长时间 EEG 记录中具有较高的灵敏度和较低的假阳性率。
