Dynamic approximate entropy with band filtering for patient´s EEG consciousness analysis

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.