This study investigates the properties of the brain electrical activity from different recording regions and physiological states for seizure detection. Neurophysiologists will find the work useful in the timely and accurate detection of epileptic seizures of their patients. We explored the best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG). Signals used in this work are 23.6 s segments of 100 single channel surface EEG recordings collected with the sampling rate of 173.61 Hz. The recorded signals are from five healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from five epilepsy patients during the seizure-free interval as well as epileptic seizures. Feature engineering was done using; i) feature extraction of each EEG wave in time, frequency and time-frequency domains via Butterworth filter, Fourier Transform and Wavelet Transform respectively and, ii) feature selection with T-test, and Sequential Forward Floating Selection (SFFS). SVM and KNN learning algorithms were applied to classify preprocessed EEG signal. Performance comparison was based on Accuracy, Sensitivity and Specificity. Our experiments showed that SVM has a slight edge over KNN.

本研究调查了来自不同记录区域和生理状态的脑电活动特性，用于癫痫检测。神经生理学家会发现这项工作有助于及时准确地检测患者的癫痫发作。我们探索了从癫痫脑电图 (EEG) 中检测有意义模式的最佳方法。这项工作中使用的信号是 100 个单通道表面 EEG 记录的 23.6 s 片段，以 173.61 Hz 的采样率收集。记录的信号来自五名闭眼和睁眼的健康志愿者，以及五名癫痫患者在无癫痫发作期间和癫痫发作期间的颅内脑电图记录。特征工程是使用；i) 及时提取每个 EEG 波的特征，频率和时频域分别通过巴特沃斯滤波器、傅里叶变换和小波变换，以及 ii) 使用 T 检验的特征选择和顺序前向浮动选择 (SFFS)。应用 SVM 和 KNN 学习算法对预处理后的脑电信号进行分类。性能比较基于准确性、敏感性和特异性。我们的实验表明，SVM 比 KNN 稍有优势。