Savadkoohi Marzieh, Oladunni Timothy, Thompson Lara
School of Engineering and Applied Sciences, University of District of Columbia, Washington DC, USA.
Department of Computer Science, University of District of Columbia, Washington DC, USA.
Biocybern Biomed Eng. 2020 Jul-Sep;40(3):1328-1341. doi: 10.1016/j.bbe.2020.07.004. Epub 2020 Jul 16.
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大脑电活动特性，用于癫痫检测。神经生理学家会发现这项工作对于及时、准确地检测其患者的癫痫发作很有用。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。本研究中使用的信号是100个单通道表面EEG记录的23.6秒片段，采样率为173.61Hz。记录的信号来自5名闭眼和睁眼的健康志愿者，以及5名癫痫患者在无癫痫发作间期和癫痫发作期间的颅内EEG记录。特征工程通过以下方式完成：i）分别通过巴特沃斯滤波器、傅里叶变换和小波变换在时域、频域和时频域对每个EEG波进行特征提取；ii）使用T检验和顺序前向浮动选择（SFFS）进行特征选择。应用支持向量机（SVM）和k近邻（KNN）学习算法对预处理后的EEG信号进行分类。性能比较基于准确率、灵敏度和特异性。我们的实验表明，SVM比KNN略有优势。
