A novel approach for detection of consciousness level in comatose patients from EEG signals with 1-D convolutional neural network

Coma is an unresponsive state of unconsciousness from which a person cannot be awakened. Glasgow Coma Score (GCS) is a clinical scale for determining the depth and length of a coma. GCS plays an important role in effective and accurate patient evaluation and is critical in planning the right treatment modalities and patient care because it shows patient outcomes and is a measurement performed several times a day. The GCS is universally accepted as a gold standard and validated scale for assessing a patient's level of consciousness. However, the scale's success has been questioned due to variations in interobserver reliability performance. In this study, the data set generated from Electroencephalography (EEG) signals obtained from 39 comatose patients was used in the training of deep neural networks for the classification of consciousness level. The EEG signals were recorded during nurse and family interaction with comatose patients. The level of consciousness was classified with the proposed 1D-CNN model. Consequently, the two classes that we label as low and high consciousness are classified with 83.3% accuracy. To our best knowledge, no prior studies are using 1D-CNN for the classification of EEG-based level of consciousness using the proposed recording process. Our study is unique from other studies in terms of recording procedure and methods.