ArticleScientific reports2019
Sleep stage classification from heart-rate variability using long short-term memory neural networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Sleep stage classification from ECG using machine learning: Evaluating the impact of signal duration.Neurobiology of sleep and circadian rhythms · 2026Article
- Article
- Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation.BMC medical informatics and decision making · 2026Article
- AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights From an Apple Watch Study.IEEE transactions on bio-medical engineering · 2026Article
- Heart rate and sleep history encode ultradian REM sleep timing.Current biology : CB · 2026Article
- Modified scoring criteria to improve the accuracy of the home sleep apnea test.Sleep & breathing = Schlaf & Atmung · 2026Article
- Predicting Sleep and Sleep Stage in Children Using Actigraphy and Heartrate via a Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation.Journal of sleep research · 2026Article
- Detecting sleep stages after stroke using wearable sensors: machine learning design and challenges.BMJ digital health & AI · 2026Article
- Evaluating sleep quality in a non-intrusive manner using contactless ballistocardiography and audio signals through a LSTM-TCN machine learning model.Frontiers in network physiology · 2026Article
- What matters beyond model choice for wearable sleep staging? How personalization, evaluation choices, and easy-to-classify wake impact performance.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Implementation of a Stress Biomarker and Development of a Deep Neural Network-Based Multi-Mental State Classification Model.Bioengineering (Basel, Switzerland) · 2025Article
- Unconstrained deep learning-based sleep stage classification using cardiorespiratory and body movement activities in adults with suspected sleep apnea.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2025Article
- Improved non-invasive detection of sleep stages when combining skin sympathetic nerve activity and heart rate variability analysis with AI.Scientific reports · 2025Article
- Article
- A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cardiovascular Risk Assessment via Sleep Patterns and ECG-Based Biological Age Estimation.Journal of clinical medicine · 2025Article
- Frailty identification using a sensor-based upper-extremity function test: a deep learning approach.Scientific reports · 2025Article
- A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability.Sleep and biological rhythms · 2025Review
- Autonomic function classification and sleep quality among young adults in Central Java, Indonesia: A cluster analysis.Preventive medicine reports · 2025Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen's k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.
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