SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

Abstract

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and Cohen's Kappa coefficient = 0.79. Our developed model is ready to test with more sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.

References in corpus (8)

• SMOTE: Synthetic Minority Over-sampling Technique
• Sequence to Sequence Learning with Neural Networks
• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
• Deep Reinforcement Learning: An Overview
• SLEEPNET: Automated Sleep Staging System via Deep Learning
• Learning to predict where to look in interactive environments using deep recurrent q-learning
• Predictive Modeling of Biomedical Signals Using Controlled Spatial Transformation

Cited by in corpus (23)

• SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
• XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
• Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions
• SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
• 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
• U-Sleep's resilience to AASM guidelines
• Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks
• Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
• Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring
• Automatic detection of microsleep episodes with deep learning
• SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging
• Multi-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks
• Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models
• TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings
• A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
• CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
• DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates
• EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
• Self-supervised Contrastive Learning for EEG-based Sleep Staging
• Contrastive Learning for Sleep Staging based on Inter Subject Correlation
• NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring
• An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification