Abstract
In this paper, we propose a Electroencephalography(EEG) signal processing method for the purpose of supporting the patient's EEG consciousness analysis. Approximate entropy(ApEn), as a complexity based method appears to have potential application to physiological and clinical time-series data. Therefore, we present an ApEn based statistical measure for patient's EEG consciousness analysis. However, it is found that high frequency noise such as electronic interference and its harmonic from the surrounding containing in the real-life recorded EEG lead to inconsistent ApEn result. To solve this problem, first we design a bandstop filter to filter high frequency noise. Then the proposed method is supported by analysis on a real world example of distinguishing between the brain consciousness states of coma and brain death. The experimental results demonstrate the effectiveness and performance of the proposed method in patient's EEG consciousness analysis.