IEEE J Biomed Health Inform. 2018 Jan;22(1):154-160. doi: 10.1109/JBHI.2017.2703873. Epub 2017 May 12.
Epilepsy is one of the most common neurological disorders in the world. Prompt detection of seizure onset from electroencephalogram (EEG) signals can improve the treatment of epileptic patients. This paper presents a new adaptive patient-specific seizure onset detection framework that dynamically selects a feature from enhanced EEG signals to discriminate seizures from normal brain activity. The proposed framework employs principal component analysis and common spatial patterns to enhance the EEG signals and uses the extracted discriminative feature as an input for adaptive distance-based change point detector to identify the seizure onsets. Experimental results from the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset show the computational efficiency of the proposed method (analyzing EEG signals in a time window of 3 s within 0.1 s using a Core i7 PC) while providing comparable results to the existing methods in terms of average sensitivity, latency, and false detection rate. The proposed method is advantageous for real-time monitoring of epileptic patients and could be used to improve early diagnosis and treatment of patients suffering from recurrent seizures.
癫痫是世界上最常见的神经障碍之一。从脑电图（EEG）信号中快速检测到癫痫发作可以改善癫痫患者的治疗效果。本文提出了一种新的自适应患者特异性癫痫发作检测框架，该框架可以从增强的 EEG 信号中动态选择特征，从而区分癫痫发作和正常脑活动。所提出的框架使用主成分分析和公共空间模式来增强 EEG 信号，并将提取的判别特征作为自适应基于距离的变点检测器的输入，以识别癫痫发作的起始。来自波士顿儿童医院-麻省理工学院（CHB-MIT）数据集的实验结果表明，该方法具有计算效率（使用 Core i7 PC 在 3 秒的时间窗口内分析 EEG 信号，在 0.1 秒内完成），并且在平均灵敏度、潜伏期和误检率方面与现有方法相比具有可比性。该方法有利于癫痫患者的实时监测，并可用于改善反复发作的患者的早期诊断和治疗。
