Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness

Abstract

Disorders of consciousness (DOC) are often related to serious changes in sleep structure. This article presents a sleep evaluation algorithm that scores the sleep structure of DOC patients to assist in assessing their consciousness level. The sleep evaluation algorithm is divided into two parts: 1) automatic sleep staging model: convolutional neural networks (CNNs) are employed for the extraction of signal features from electroencephalogram (EEG) and electrooculogram (EOG), and bidirectional long short-term memory (Bi-LSTM) with attention mechanism is applied to learn sequential information; and 2) consciousness assessment: automated sleep staging results are used to extract consciousness-related sleep features that are utilized by a support vector machine (SVM) classifier to assess consciousness. In this study, the CNN-BiLSTM model with an attention sleep network (CBASleepNet) was conducted using the sleep-EDF and MASS datasets. The experimental results demonstrated the effectiveness of the proposed model, which outperformed similar models. Moreover, CBASleepNet was applied to sleep staging in DOC patients through transfer learning and fine-tuning. Consciousness assessments were conducted on seven minimally conscious state (MCS) patients and four vegetative state (VS)/unresponsive wakefulness syndrome (UWS) patients, achieving an overall accuracy of 81.8%. The sleep evaluation algorithm can be used to evaluate the consciousness level of patients effectively.