SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Abstract

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet. At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modelling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.

This article has been published in IEEE Transactions on Neural Systems and Rehabilitation Engineering

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Natural Language Processing (almost) from Scratch
• End to End Learning for Self-Driving Cars
• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• End-to-End Training of Deep Visuomotor Policies
• A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
• Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
• Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
• Mixed Neural Network Approach for Temporal Sleep Stage Classification
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
• Personalizing deep learning models for automatic sleep staging
• Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

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• CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
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• EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
• sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging
• Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification
• Screening for REM Sleep Behaviour Disorder with Minimal Sensors
• A Language-Guided Multimodal Foundation Model for Zero-Shot and Multi-Task Brain Signal Analysis