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

Phan, Huy (2019) Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019). . IEEE (In press)

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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.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Automatic sleep staging, end-to-end, sequence-to-sequence, deep learning, model fusion
Divisions: Faculties > Sciences > School of Computing
Depositing User: Huy Phan
Date Deposited: 07 May 2019 12:58 UTC
Last Modified: 27 Jul 2019 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
Phan, Huy:
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