Patients' consciousness analysis using dynamic approximate entropy and MEMD method

Gaochao Cui, Yunchao Yin, Qibin Zhao, Andrzej Cichocki, Jianting Cao

Abstract: Electroencephalography (EEG) based preliminary examination has been proposed in the clinical brain death determination. Multivariate empirical mode decomposition(MEMD) and approximate entropy(ApEn) are often used in the EEG signal analysis process. MEMD is an extended approach of empirical mode decomposition(EMD), in which it overcomes the problem of the decomposed number and frequency, and enables to extract brain activity features from multi-channel EEG simultaneously. ApEn as a complexity based method appears to have potential for the application to physiological and clinical time series data. In our previous studies, MEMD method and ApEn measure were always used severally, if MEMD and ApEn are used to analysis the same EEG signal simultaneously, the result of experiment will be more accurate. In this paper, we present MEMD method and ApEn measure based blind test without knowing about the clinical symptoms of patients beforehand. Features obtained from two typical cases indicate one patient being in coma and another in quasi-brain-death state.