In this chapter, we present several electroencephalography (EEG) signal processing and statistical analysis methods for the purpose of clinical diagnosis of brain death, in which an EEG-based preliminary examination system was developed during the standard clinical procedure. Specifically, given the reallife recorded EEG signals, a robust principal factor analysis (PFA) associated with independent component analysis (ICA) approach is applied to reduce the power of additive noise and to further separate the brain waves and interference signals. We also propose a few frequency-based and complexity-based statistics for quantitative EEG analysis with an aim to evaluate the statistical significance differences between the coma patients and quasi-brain-death patients. Based on feature selection and classification, the system may yield a binary decision from the classifier with regard to the patient's status. Our empirical data analysis has shown some promising directions for real-time EEG analysis in clinical practice.
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Cao, J., Chen, Z. (2008). Advanced EEG Signal Processing in Brain Death Diagnosis. In: Mandic, D., Golz, M., Kuh, A., Obradovic, D., Tanaka, T. (eds) Signal Processing Techniques for Knowledge Extraction and Information Fusion. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74367-7_15
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