Application of Signal Detection of Epileptic Spike with Magnetoencephalogram Based on 2DPCA

doi:10.3778/j.issn.1002-8331.1709-0286

Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (1): 186-190.DOI: 10.3778/j.issn.1002-8331.1709-0286

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Application of Signal Detection of Epileptic Spike with Magnetoencephalogram Based on 2DPCA

YANG Baoshan1，2，3, HU Yegang1，2，3, ZHANG Jicong1，2，3

1.School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
2.Advanced Innovation Center for Biomedical Engineering, ?Beihang University, Beijing 100191, China
3.Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China

Online:2019-01-01 Published:2019-01-07

2DPCA在脑磁图棘波信号检测中的应用

杨宝山1，2，3，胡业刚1，2，3，张冀聪1，2，3

1.北京航空航天大学生物与医学工程学院，北京 100191
2.北京航空航天大学生物医学工程高精尖创新中心，北京 100191
3.北京航空航天大学大数据精准医疗高精尖创新中心，北京 100191

Abstract

Abstract: The epileptic spike plays a key role in the epileptic diagnosis and the epileptogenic zone evaluation, additionally, the Magnetoencephalogram（MEG） equipment can capture the interictal spikes more accurately. However, clinicians still manually mark spikes due to lacking an effective multi-channel spike detection approach. To solve this problem, this paper proposes a spike detection algorithm based on multi-channel MEG signals. The multi-channel MEG signal within a configurable time window is considered as a two-dimensional matrix. Furthermore, the intrinsic features of the matrix are extracted by the Two-Dimensional Principal Component Analysis（2DPCA） and the spikes are detected through the nearest neighbor classifier. Finally, experimental results indicate that the proposed method is effective and the detection rate can be up to 93. 23% on clinical MEG data.

Key words: signal detection, Two-Dimensional Principal Component Analysis（2DPCA）, Magnetoencephalogram（MEG）, epileptic spike

摘要： 棘波是癫痫疾病诊断和癫痫灶评估的重要标志，脑磁图设备能更精确地捕捉到癫痫患者在发作间期的棘波信号。然而，目前临床医生仍依赖于手动方法标记棘波信号，缺少便捷离线的多通道棘波检测方法。提出一种脑磁图的多通道棘波检测方法，针对给定时间宽度的多通道脑磁图信号的时间序列可以看作为一个二维矩阵，利用二维主成分分析（2DPCA）方法提取该矩阵的本征特征，再结合最近邻分类器实现离线的多通道棘波信号检测。通过临床癫痫患者的脑磁图信号验证表明，提出的方法棘波信号检测率高达93.23%，且该方法是有效的。

关键词: 信号检测, 二维主成分分析, 脑磁图, 棘波信号

YANG Baoshan1，2，3, HU Yegang1，2，3, ZHANG Jicong1，2，3. Application of Signal Detection of Epileptic Spike with Magnetoencephalogram Based on 2DPCA[J]. Computer Engineering and Applications, 2019, 55(1): 186-190.

杨宝山1，2，3，胡业刚1，2，3，张冀聪1，2，3. 2DPCA在脑磁图棘波信号检测中的应用[J]. 计算机工程与应用, 2019, 55(1): 186-190.

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Application of Signal Detection of Epileptic Spike with Magnetoencephalogram Based on 2DPCA

2DPCA在脑磁图棘波信号检测中的应用

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