Abstract
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.
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Shi, Q., Cao, J., Zhou, W., Tanaka, T., Wang, R. (2010). Dynamic Extension of Approximate Entropy Measure for Brain-Death EEG. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_44
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DOI: https://doi.org/10.1007/978-3-642-13318-3_44
Publisher Name: Springer, Berlin, Heidelberg
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