Automatic Sleep Staging: Recent Development, Challenges, and Future Directions

• URL: http://arxiv.org/abs/2111.08446v1
• Date: Wed, 3 Nov 2021 19:49:47 GMT
• Title: Automatic Sleep Staging: Recent Development, Challenges, and Future Directions
• Authors: Huy Phan, Kaare Mikkelsen
• Abstract summary: Modern deep learning holds a great potential to transform clinical practice on human sleep. Machines have been trained to mimic manual scoring, leading to similar performance to human sleep experts. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments.
• Score: 10.685136927202286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.

Related papers

• aSAGA: Automatic Sleep Analysis with Gray Areas [2.47298967960367]
  State-of-the-art automatic sleep staging methods have already demonstrated comparable reliability and superior time efficiency to manual sleep staging. We propose a human-in-the-loop concept for sleep analysis, presenting an automatic sleep staging model (aSAGA) that performs effectively with both clinical polysomnographic recordings and home sleep studies.
  arXiv  Detail & Related papers  (2023-10-03T13:17:38Z)
• Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals [35.141080847207355]
  Sleep stage classification based on deep brain recording has great potential to provide more precise treatment for patients. We proposed an applicable cross-modal transfer learning method for sleep stage classification with implanted devices. We tested the model with deep brain recording data from 12 patients with Parkinson's disease.
  arXiv  Detail & Related papers  (2023-02-07T03:21:33Z)
• Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data [67.60224656603823]
  Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
  arXiv  Detail & Related papers  (2023-01-17T15:18:45Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling [51.316408685035526]
  Learning new tasks and skills in succession without losing prior learning is a computational challenge for both artificial and biological neural networks. Here, we investigate how modeling three distinct components of mammalian sleep together affects continual learning in artificial neural networks.
  arXiv  Detail & Related papers  (2022-09-09T13:45:27Z)
• TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging [2.105172041656126]
  We propose a novel deep neural network structure, TransSleep, that captures distinctive local temporal patterns. Results show that TransSleep achieves promising performance in automatic sleep staging.
  arXiv  Detail & Related papers  (2022-03-22T08:55:32Z)
• Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
  Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge. We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
  arXiv  Detail & Related papers  (2021-06-15T22:06:39Z)
• A preliminary study on evaluating Consultation Notes with Post-Editing [67.30200768442926]
  We propose a semi-automatic approach whereby physicians post-edit generated notes before submitting them. We conduct a preliminary study on the time saving of automatically generated consultation notes with post-editing. We time this and find that it is faster than writing the note from scratch.
  arXiv  Detail & Related papers  (2021-04-09T14:42:00Z)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)
• MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning [15.451212330924447]
  We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML) In comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification.
  arXiv  Detail & Related papers  (2020-04-08T16:31:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.