DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates

Abstract

Deep learning is widely used in the most recent automatic sleep scoring algorithms. Its popularity stems from its excellent performance and from its ability to directly process raw signals and to learn feature from the data. Most of the existing scoring algorithms exploit very computationally demanding architectures, due to their high number of training parameters, and process lengthy time sequences in input (up to 12 minutes). Only few of these architectures provide an estimate of the model uncertainty. In this study we propose DeepSleepNet-Lite, a simplified and lightweight scoring architecture, processing only 90-seconds EEG input sequences. We exploit, for the first time in sleep scoring, the Monte Carlo dropout technique to enhance the performance of the architecture and to also detect the uncertain instances. The evaluation is performed on a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database. DeepSleepNet-Lite achieves slightly lower performance, if not on par, compared to the existing state-of-the-art architectures, in overall accuracy, macro F1-score and Cohen's kappa (on Sleep-EDF v1-2013 +/-30mins: 84.0%, 78.0%, 0.78; on Sleep-EDF v2-2018 +/-30mins: 80.3%, 75.2%, 0.73). Monte Carlo dropout enables the estimate of the uncertain predictions. By rejecting the uncertain instances, the model achieves higher performance on both versions of the database (on Sleep-EDF v1-2013 +/-30mins: 86.1.0%, 79.6%, 0.81; on Sleep-EDF v2-2018 +/-30mins: 82.3%, 76.7%, 0.76). Our lighter sleep scoring approach paves the way to the application of scoring algorithms for sleep analysis in real-time.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
• SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
• Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
• Mixed Neural Network Approach for Temporal Sleep Stage Classification
• XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
• Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG
• Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
• Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms