Patients' EEG Data Analysis via Spectrogram Image with a Convolution Neural Network

Electroencephalogram (EEG) recording is relatively safe for the patients who are in deep coma or quasi brain death, so it is often used to verify the diagnosis of brain death in clinical practice. The objective of this paper is to apply deep learning method to EEG signal analysis in order to confirm clinical brain death diagnosis. A novel approach using spectrogram images produced from EEG signals as the input dataset of Convolution Neural Network (CNN) is proposed in this paper. A deep CNN was trained to obtain the similarity degree of the patients' EEG signals with the clinical diagnosed symptoms. This method can evaluate the condition of the brain damage patients and can be a reliable reference of quasi brain death diagnosis.