University of Twente Student Theses


Deep sleep stage detection

Lu, C. (2020) Deep sleep stage detection.

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Abstract:Sleep quality is very important to human health. To detect sleep disorders, sleep scoring is performed by sleep experts on the polysomnograms that record the activities of different parts of the human body, like electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG). Current automatic sleep scoring approaches are mostly based on single-channel EEGs and the few multi-channel models that exist do not obtain a satisfying performance. In this master assignment, we firstly perform a module evaluation to test the performance of useful deep learning modules developed for optimizing single-channel models in multi-channel sleep scoring. Based on the results, we build a well-performing multi-channel automatic sleep scoring model, where, temporal learning is applied to extract temporal features from sleep epochs, spatial learning is designed to capture correlation information among the channels of a modality, sequential learning is performed to extract transition rules from sleep sequences and the residual connection is used to consider temporal and sequential information together for sleep stage classification. We evaluate our model on two public datasets — the SleepEDF-13 and SHHS-1 datasets. Our model obtains an accuracy of 84.6%, macro F1 score of 78.3% and Cohen’s kappa of 0.79 for the SleepEDF-13 dataset and an accuracy of 86.4%, macro F1 score of 77.7% and Cohen’s kappa of 0.81 for the SHHS-1 dataset. Additionally, we employ two methods — the layer-wise relevance propagation (LRP) and an embedded channel attention network (Embedded CAN) to investigate the channel and feature importance in automatic sleep scoring. Results show that our multi-channel sleep scoring model performs well on different datasets compared to the state-of-the-art, and channel and feature importance obtained comply with the AASM rules and can be a guidance for further optimizing automatic sleep scoring models.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:44 medicine, 54 computer science
Programme:Computer Science MSc (60300)
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