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Automatic sleep staging of EEG signals: recent development, challenges, and future directions

PHYSIOLOGICAL MEASUREMENT (2022)

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SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification

Huy Phan et al.

Summary: In this study, a sleep-staging model called SleepTransformer is proposed, which is based on the Transformer backbone and offers interpretability and uncertainty quantification. The results show that SleepTransformer performs well on two different-sized databases. This research holds promise for integration into clinical settings.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

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Article Engineering, Biomedical

Unsupervised Domain Adaptation by Statistics Alignment for Deep Sleep Staging Networks

Jiahao Fan et al.

Summary: In this study, an unsupervised domain adaptation method called domain statistics alignment (DSA) is proposed to adapt well-trained models from large-scale datasets to small sleep cohorts. By modulating domain-specific statistics of deep features, DSA improves the performance of source models on target domains. The proposed methods are validated on multiple sleep databases, demonstrating significant performance improvement on target domains.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2022)

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Article Engineering, Biomedical

Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations

Kaare B. Mikkelsen et al.

Summary: Modern sleep monitoring is turning towards the use of unobtrusive sensors and automatic sleep scoring algorithms. Sensor positions in the facial area and around the ears, which are relatively hairless, are gaining extra attention. The study found that large electrode distances render positioning less critical for achieving accurate sleep scoring.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

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Article Computer Science, Information Systems

Transferring Structured Knowledge in Unsupervised Domain Adaptation of a Sleep Staging Network

Chaehwa Yoo et al.

Summary: This study proposes an unsupervised domain adaptation method for automatic sleep staging based on deep learning, which significantly improves the performance of sleep staging by re-aligning data distributions between different domains and generating domain-invariant features.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

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Article Computer Science, Artificial Intelligence

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

Huy Phan et al.

Summary: This study proposes a sequence-to-sequence sleep staging model, XSleepNet, that can learn a joint representation from both raw signals and time-frequency images. The model adapts the learning pace for each view based on their generalization and overfitting behavior. Experimental results show that XSleepNet consistently outperforms single-view baselines and a multi-view baseline with a simple fusion strategy, achieving state-of-the-art results on different databases.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

Mustafa Radha et al.

Summary: This study successfully applied transfer learning to adapt a deep recurrent neural network trained on a large sleep data set to a smaller PPG data set, achieving impressive results and advancing the field of home sleep stage monitoring.

NPJ DIGITAL MEDICINE (2021)

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Article Clinical Neurology

Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders

Laure Peter-Derex et al.

Summary: The study evaluated the performance of single-channel automatic sleep staging software in adult patients with various sleep disorders, showing comparable agreement with visual staging to healthy individuals and interexpert agreement. The software could be considered as a complementary tool in clinical practice for sleep stage scoring after improvement of rapid eye movement sleep onset detection.

JOURNAL OF CLINICAL SLEEP MEDICINE (2021)

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Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

Alexander Neergaard Olesen et al.

Summary: The study examined the generalizability of a novel deep neural network across multiple large-scale cohorts and found that overall classification accuracy improved with increasing fractions of training data and number of data sources. Different cohorts showed varying levels of generalization to other cohorts. The study suggests that automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization.

SLEEP (2021)

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal

Hongyang Li et al.

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A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Syed Anas Imtiaz

Summary: Designing wearable systems for sleep detection and staging is challenging due to various constraints, with the most common sensing modalities being EEG-based and PPG-based systems. EEG-based systems are the most accurate, capable of identifying all sleep stages, while PPG-based systems are simpler to use for wearable monitoring but cannot identify all sleep stages.

SENSORS (2021)

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Review Clinical Neurology

A review on current trends in automatic sleep staging through bio-signal recordings and future challenges

Panteleimon Chriskos et al.

Summary: Sleep staging is a crucial process for analyzing polysomnographic data, and many automatic methods have been proposed to aid in quick interpretation. These methods rely on bio-signal recordings like EEG and ECG, but there is still a need for more advanced and streamlined evaluation to enhance data interpretation. This review focuses on current and future challenges in automatic sleep staging methods through bio-signal recording.

SLEEP MEDICINE REVIEWS (2021)

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EEGs Vary Less Between Lab and Home Locations Than They Do Between People

Kaare B. Mikkelsen et al.

Summary: This study designed an EEG paradigm suitable for deployment in the wild, tested on 20 subjects, showing that the location variation for this paradigm is less than the inter subject variation. Therefore, if a research paradigm benefits from being performed in less controlled environments, limited problems are expected when doing so.

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Uncovering the structure of clinical EEG signals with self-supervised learning

Hubert Banville et al.

Summary: This study explores the application of self-supervised learning in EEG data, showing that linear classifiers trained on SSL-learned features outperformed purely supervised deep neural networks in low-labeled data regimes and achieved competitive performance when all labels were available. Furthermore, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena.

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272 Long-term monitoring of trait-like characteristics of the sleep electroencephalogram using ear-EEG

Martin Hemmsen et al.

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SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

Maytus Piriyajitakonkij et al.

Summary: This study investigates the performance of UWB radar for classifying human sleep postures, proposing SPN which effectively captures time and frequency features and outperforms DCNN in activity recognition.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

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Article Neurosciences

CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

Wenpeng Neng et al.

Summary: In this paper, a novel deep learning model CCRRSleepNet based on hybrid relational inductive biases is proposed, which divides single channel EEG data into three levels and applies various relational inductive biases. By optimizing the model with matching nonrelational inductive biases, superior experimental results are achieved compared to many existing methods.

BRAIN SCIENCES (2021)

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Unsupervised sleep staging system based on domain adaptation

Ranqi Zhao et al.

Summary: Most deep-learning-based sleep staging systems rely on labeled physiological signals, but manual labeling is time-consuming. Physiological differences among subjects make model generalization challenging, and training unlabeled data through domain adaptation can effectively address data insufficiency.

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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Huy Phan et al.

Summary: The study introduces a deep transfer learning approach to address data-variability and data-inefficiency issues in automatic sleep staging for small cohorts, demonstrating significant performance improvement on target domains.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2021)

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MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

Nannapas Banluesombatkul et al.

Summary: This study proposes a novel transfer learning framework MetaSleepLearner based on Meta-Learning (MAML), achieving a range of 5.4% to 17.7% improvement in comparison to the conventional approach. The model interpretation after adaptation to each subject confirms a reasonable learning performance, showcasing potential for human-machine collaboration in sleep stage classification and reducing clinicians' workload.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

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Xue Jiang et al.

Summary: This paper proposes a self-supervised contrastive learning method of EEG signals for sleep stage classification, which addresses the issue of data shortage in supervised learning by utilizing unlabeled data, and achieves competitive performance in empirical evaluations.

2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2021)

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

Luigi Fiorillo et al.

Summary: Deep learning is widely used in automatic sleep scoring algorithms, with the newly proposed DeepSleepNet-Lite showing promising performance by utilizing a simplified architecture and the Monte Carlo dropout technique for enhancing performance and detecting uncertain instances. The evaluation on EEG data from the Sleep-EDF database demonstrates competitive performance compared to existing state-of-the-art architectures, with the rejection of uncertain instances leading to improved results. This lighter sleep scoring approach opens up possibilities for real-time application of sleep analysis algorithms.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

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Proceedings Paper Computer Science, Theory & Methods

Automatic Sleep Staging using a Small-footprint Sensor Array and Recurrent-Convolutional Neural Networks

William G. Coon et al.

Summary: Personalized precision medicine and tele-healthcare are on the rise, giving individuals access to their health data; a growing gap between wakeful and nocturnal health highlights the need for accurate automated home sleep measurement methods; a small sensor array with electrode stickers for self-application on the forehead achieves high accuracy and avoids noise issues.

2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) (2021)

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RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

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