EEG data analysis based on EMD for coma and quasi-brain-death patients
Journal of Experimental & Theoretical Artificial Intelligence - Advances in knowledge discovery and data analysis for artificial intelligence
Analysis of the quasi-brain-death EEG data based on a robust ICA approach
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
An application of translation error to brain death diagnosis
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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In this paper, we propose a Electroencephalography (EEG) signal processing method for the purpose of supporting the clinical diagnosis of brain death Approximate entropy (ApEn), as a complexity-based method appears to have potential application to physiological and clinical time-series data Therefore, we present a ApEn based statistical measure for brain-death EEG analysis Measure crossing all channels extends along the time-coordinate of EEG signal to observe the variation of the dynamic complexity However, it is found that high frequency noise such as electronic interference from the surrounding containing in the real-life recorded EEG lead to inconsistent ApEn result To solve this problem, in our method, a processing approach of EEG signal denoising is proposed by using empirical mode decomposition (EMD) Thus, high frequency interference component can be discarded from the noisy period along the time-coordinate of EEG signals The experimental results demonstrate the effectiveness of proposed method and the accuracy of this dynamic complexity measure is well improved.