期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：3
页码：1149-1158
DOI：10.12928/telkomnika.v17i3.11776
出版社：Universitas Ahmad Dahlan
摘要：Stroke patients require a long recovery. One success of the treatment given is the evaluation and
monitoring during recovery. One device for monitoring the development of post-stroke patients is
Electroencephalogram (EEG). This research proposed a method for extracting variables of EEG signals for
post-stroke patient analysis using Wavelet and Self-Organizing Map Kohonen clustering. EEG signal was
extracted by Wavelet to obtain Alpha, beta, theta, gamma, and Mu waves. These waves, the amplitude
and asymmetric of the symmetric channel pairs are features in Self Organizing Map Kohonen Clustering.
Clustering results were compared with actual clusters of post-stroke and no-stroke subjects to extract
significant variable. These results showed that the configuration of Alpha, Beta, and Mu waves, amplitude
together with the difference between the variable of symmetric channel pairs are significant in the analysis
of post-stroke patients. The results gave using symmetric channel pairs provided 54-74% accuracy.
其他摘要：Stroke patients require a long recovery. One success of the treatment given is the evaluation and monitoring during recovery. One device for monitoring the development of post-stroke patients is Electroencephalogram (EEG). This research proposed a method for extracting variables of EEG signals for post-stroke patient analysis using Wavelet and Self-Organizing Map Kohonen clustering. EEG signal was extracted by Wavelet to obtain Alpha, beta, theta, gamma, and Mu waves. These waves, the amplitude and asymmetric of the symmetric channel pairs are features in Self Organizing Map Kohonen Clustering. Clustering results were compared with actual clusters of post-stroke and no-stroke subjects to extract significant variable. These results showed that the configuration of Alpha, Beta, and Mu waves, amplitude together with the difference between the variable of symmetric channel pairs are significant in the analysis of post-stroke patients. The results gave using symmetric channel pairs provided 54-74% accuracy.
