EEG Analysis for Differentiating between Brain Death and Coma in Humans
To have an accuracy and fast diagnosis of brain death is very urgent for patients especially for distinguishing brain death from coma circumstance. Electroencephalogram is a method of invasive recording brain signals with high temporal resolution. So in clinics, EEG is taken as the most reliable method to estimate the brain death state of affairs. The objective of this paper is to find out the statistical rule on brain death and coma detection through EEG signal processing technology analysis on 20 patientsâ€™ medical data (10 for brain death group, 10 for coma group) and then hope to give aid diagnosis conclusions in clinical practice. First, the independent component analysis (ICA) has been applied to solve artifact removal problem combined with wavelet decomposition method to compute the signal noise ratio (SNR). Moreover, the Fourier transform is calculated to compare the power spectrum of the two groups. In addition, we applied the multi-scale permutation entropy into comparison with complexity of brain death and coma patientsâ€™ data in different brain inter-rhythms. Finally, the Wilcoxon rank sum has been used to test their statistics significant differences. We proposed our understanding and conclusions on this experiment results. Specially, the real-time brain death and coma patientsâ€™ distinguishing is discussed based on parallel computational idea such as cloud computing on Hadoop.
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