Deep Convolutional Neural Network for Detection of Disorders of Consciousness

The diagnosis of consciousness has always been a major challenge in clinical diagnosis. Resent researches prove that machine learning has a powerful ability to distinguish between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). What's more, convolutional neural network has made great progress in electroencephalography (EEG) analysis of other disorders. As a result, an improved 1D-convolutional neural network structure has been proposed for outcome prediction, using resting-state EEG signals from patients with disorders of consciousness. The model is established by training 690 EEG segments from 34 of MCS and 35 of UWS diagnosed by Coma Recovery Scale - Revised. The experimental results show that the accuracy, positive predictive value, specificity and sensitivity of the improved model in our research are 88.84%, 85.59%, 86.79% and 91.22%, respectively. It shows that our improved model has better performance than the model without Batch Normalization layer, as well as the model with deep graph convolutional neural network. The improved ID-convolutional neural network model in this study can be used as an auxiliary medical method for clinical diagnosis and detection of consciousness disorders. More profoundly, it could drive the development of robust expert systems in other neurological diseases.