Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging.

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Abstract

Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. The fusion model is composed of two base sleep-stage classifiers, SeqSleepNet and DeepSleepNet, both of which are state-of-the-art end-to-end deep learning models complying to the sequence-to-sequence sleep staging scheme. In addition, in the light of ensemble methods, we reason and demonstrate that these two networks form a good ensemble of models due to their high diversity. Experiments show that the fusion approach is able to preserve the strength of the base networks in the fusion model, leading to consistent performance gains over the two base networks. The fusion model obtain the best modelling results we have observed so far on the Montreal Archive of Sleep Studies (MASS) dataset with 200 subjects, achieving an overall accuracy of 88.0%, a macro F1-score of 84.3%, and a Cohen’s kappa of 0.828.