Abstract：EEG is the most important tool in the diagnosis and treatment of epilepsy, and the analysis of large data volume of EEG records has brought considerable difficulties, which may be overcome by the automatic computer assistant classification tools. In this paper, the coupling between the phase of low frequency and the amplitude of high frequency was studied from the perspective of phase amplitude modulation, and the normalized modulation index (MI) was further used to quantify the coupling intensity in different frequency pairs. The Bonn datasets with total 200 samples of interictal and ictal EEG segment were used in this study. A method of partitioning the MI map according to the ranges of high and low frequency rhythms was proposed. Results have shown that the MI value between Gamma and Delta (2～4 Hz) rhythm was significantly increased (P<0.01) from the interictal state (0.0036±0.0087) to the ictal state (0.0099±0.0096). The MI value of Theta (4～8 Hz) Gamma was significantly increased (P<0.01) from the interictal state (0.0014±0.0032) to ictal state (0.0087±0.0062). Modulation strength of Theta Beta rhythm also increased significantly from interictal state (0.0005±0.0007) to ictal state (0.0022±0.0013). The classification accuracy based on MI characteristics was 97% for the distinguishing the ictal data, and the accuracy was not changed much by using support vector machine with five folder cross validation and random forest. The using of proposed method would greatly improve the efficiency of clinical video EEG analysis for determining the ictal state.
曹春雨,韩凌,李春胜. 癫痫发作脑电信号的相位幅度调制研究[J]. 中国生物医学工程学报, 2018, 37(1): 33-39.
Cao Chunyu， Han Ling， Li Chunsheng. Research on EEG Phase Amplitude Modulation during Epileptic Seizure. Chinese Journal of Biomedical Engineering, 2018, 37(1): 33-39.
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