机构：[1]School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China[2]Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China[3]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine. Beihang University, Beijing 100191, China[4]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China神经内科首都医科大学宣武医院
Spike detection plays a key role in clinical diagnosis of epilepsy. Intracranial EEG is mainly used to locate the lesion according to the location and number of spikes before the epilepsy surgery. Many spike detection methods have been adopted for scalp EEG, but few of them aimed at intracranial EEG. So this paper proposes a novel spike detection algorithm using frequency-band amplitude feature and kernel support vector machine classifier for intracranial EEG data. The algorithm consists of two steps. In the first step, a fast Fourier transform algorithm computes the discrete Fourier transform of intracranial EEG, which includes the spikes and its locations marked by two expert neurologists. The total amplitude of the delta, theta, alpha, beta and gamma frequency-band is extracted as the different features, respectively. In the second step, those features are selectively used, and the kernel support vector machine is used as a classifier for training a detection model to detect spikes on the training sets. The performance of algorithm is shown to be efficient and accurate on the testing sets, and the average performance is obtained with 98.44% sensitivity, 100% selectivity and 99.54% accuracy.
基金：
National Key Research and Development program of the Ministry of Science and Technology of China (grant number 2016YFF0201002),
the Natural Science Foundation of China (grant numbers 61301005 and 61572055),
the project of Brain Functional Disease and Neuromodulation of Beijing Key Laboratory,
the Natural Science Project of National Statistical Bureau (2014LY088),
语种：
外文
被引次数：
WOS：
第一作者：
第一作者机构：[1]School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China[2]Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China[3]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine. Beihang University, Beijing 100191, China
通讯作者：
通讯机构：[1]School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China[2]Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China[3]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine. Beihang University, Beijing 100191, China
推荐引用方式(GB/T 7714)：
Baoshan Yang,Yegang Hu,Yu Zhu,et al.Intracranial EEG spike detection based on rhythm information and SVM[J].2017 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2017), VOL 2.2017,2:382-385.doi:10.1109/IHMSC.2017.197.
APA：
Baoshan Yang,Yegang Hu,Yu Zhu,Yuping Wang&Jicong Zhang.(2017).Intracranial EEG spike detection based on rhythm information and SVM.2017 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2017), VOL 2,2,
MLA：
Baoshan Yang,et al."Intracranial EEG spike detection based on rhythm information and SVM".2017 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2017), VOL 2 2.(2017):382-385
