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Visualising Convolutional Neural Network Decisions in Automatic Sleep Scoring

Andreotti, Fernando, Phan, Huy, De Vos, Maarten (2018) Visualising Convolutional Neural Network Decisions in Automatic Sleep Scoring. In: CEUR Workshop Proceedings. Joint Workshop on Artificial Intelligence in Health (AIH) 2018. . pp. 70-81. CEUR Workshop Proceedings, Stockholm, Sweden (KAR id:72666)


Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework for automated sleep classification, however, the lack of interpretability of its results has hampered CNN's further use in medicine. In this study, we train a CNN using as input Continuous Wavelet transformed EEG, EOG and EMG recordings from a publicly available dataset. The network achieved a 10-fold cross-validation Cohen's Kappa score of $\kappa = 0.71 \pm 0.01$. Further, we provide insights on how this network classifies individual epochs of sleep using Guided Gradient-weighted Class Activation Maps (Guided Grad-CAM). The proposed approach is able to produce fine-grained activation maps on time-frequency domain for each signal providing a useful tool for identifying relevant features in CNNs.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Convolutional Neural Networks, Class Activation Maps, Guided Backpropagation, Polysomnography, Wavelet Transform
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Huy Phan
Date Deposited: 25 Feb 2019 14:39 UTC
Last Modified: 09 Dec 2022 09:24 UTC
Resource URI: (The current URI for this page, for reference purposes)

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