Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers

Abstract

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo.

11 pages, 7 figures, 6 tables

References in corpus (14)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Simple Framework for Contrastive Learning of Visual Representations
• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
• SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
• OCNet: Object Context Network for Scene Parsing
• Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
• Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
• SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
• Mixed Neural Network Approach for Temporal Sleep Stage Classification
• XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
• Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
• MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
• SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning

Cited by in corpus (1)

• NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring