Selecting EEG components using time series analysis in brain death diagnosis |
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Authors: | Gen Hori Jianting Cao |
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Institution: | (1) Department of Business Administration, Asia University, Tokyo 180-8629, Japan;(2) Department of Information System, Saitama Institute of Technology, Saitama 369-0293, Japan;(3) East China University of Science and Technology, 200237 Shanghai, China;(4) Lab. for Advanced Brain Signal Processing, BSI, RIKEN, Saitama 351-0198, Japan |
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Abstract: | In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s
brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination
from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is
an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels,
we obtain several separated components among which some correspond to the brain activities while others contain artifacts.
This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in
brain death diagnosis, such automatic component selection is helpful. |
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Keywords: | Brain death diagnosis Signal processing Independent component analysis Time series analysis Wayland test |
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