CAJ | 학술논문

目的：利用匹配追踪(MP)算法的良好参数化描述特性，研究癫痫脑磁图的时频分布特征。方法：提出应用MP算法提取体感诱发磁场(SEF)信号的详细的时频成分。结果：对多例正常受试者的体感诱发脑磁图信号进行时频分析，从中提出大部分信号中都存在的时频成分，表明系列稳定的SEF时频成分可使用MP分解算法识别。结论：体感诱发信号通过MP分解，能够产生稳定和微小的时频成分，并且这些成分在时频中具有特定的位置，从而为临床脑功能和脑部疾患发病机制的研究提供可靠的研究指标。
목적：이용필배추종(MP)산법적량호삼수화묘술특성，연구전간뇌자도적시빈분포특정。방법：제출응용MP산법제취체감유발자장(SEF)신호적상세적시빈성분。결과：대다례정상수시자적체감유발뇌자도신호진행시빈분석，종중제출대부분신호중도존재적시빈성분，표명계렬은정적SEF시빈성분가사용MP분해산법식별。결론：체감유발신호통과MP분해，능구산생은정화미소적시빈성분，병차저사성분재시빈중구유특정적위치，종이위림상뇌공능화뇌부질환발병궤제적연구제공가고적연구지표。
Objective:Matching pursuit algorithm(MAP),for its good parametric characterization, is applied in Magnetoencephalography(MEG) to study time-frequency distribution. Methods:This paper proposes to apply a high-resolution time-frequency analysis algorithm, the matching pursuit (MP), to extract detailed time-frequency components of SEF signals. Results: Experimental results on cortical SEF signals of several normal subjects show that a series of stable SEF time components can be identified using the MP decomposition algorithm. Conclusion:This study shows that there is a set of stable and minute time-frequency componentsin SEF signals, which are revealed by the MP decomposition. These stable SEF components have specific localizations in the time domain and may provide a reliable index for clinical research of brain function and brain disease pathogenesis.