Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Abstract

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on CNNs for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs' labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. We conducted experiments on two public datasets: Sleep-EDF Expanded with 20 subjects, and Montreal Archive of Sleep Studies dataset with 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

arXiv commentThis article has been published in IEEE Transactions on Biomedical Engineering

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• 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
• Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions
• Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
• MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
• Automatic sleep stage classification with deep residual networks in a mixed-cohort setting
• SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
• NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
• Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
• Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
• Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
• MSED: a multi-modal sleep event detection model for clinical sleep analysis
• PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations
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• Deep Efficient Continuous Manifold Learning for Time Series Modeling
• REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
• Multi-Channel Multi-Domain based Knowledge Distillation Algorithm for Sleep Staging with Single-Channel EEG
• Multi-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks
• Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation
• Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
• Sleep Staging Based on Multi Scale Dual Attention Network
• sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging
• Self-supervised Contrastive Learning for EEG-based Sleep Staging
• NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring
• End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features