ArticleNPJ digital medicine2021
A deep transfer learning approach for wearable sleep stage classification with photoplethysmography.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 112 citations in OpenAlex.
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights From an Apple Watch Study.IEEE transactions on bio-medical engineering · 2026Article
- Sleep awake detection from leg-worn wearables using deep sensor fusion.Scientific reports · 2026Article
- Large-Scale Validation of a Dual Cross-Attention Network for Automated Sleep Staging Using Wearable Photoplethysmography Signals.Diagnostics (Basel, Switzerland) · 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
- Article
- Skin-Integrated Soft Wearable XR Interfaces for Seamless and Realistic User Experience.Chemical reviews · 2025Review
- An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.Engineering applications of artificial intelligence · 2025Article
- Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals.Nature communications · 2025Article
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health.NPJ digital medicine · 2025Article
- Detecting arousals and sleep from respiratory inductance plethysmography.Sleep & breathing = Schlaf & Atmung · 2025Article
- Editorial: Biological and digital markers in sleep, circadian rhythm and epilepsy using artificial intelligence.Frontiers in physiology · 2025Article
- Non-obtrusive monitoring of obstructive sleep apnea syndrome based on ballistocardiography: a preliminary study.Frontiers in neuroscience · 2025Article
- Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.NPJ cardiovascular health · 2025Article
- Article
- A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study.International journal of clinical pharmacy · 2024Article
- Generalized sleep decoding with basal ganglia signals in multiple movement disorders.NPJ digital medicine · 2024Article
- Photoplethysmography based atrial fibrillation detection: a continually growing field.Physiological measurement · 2024Review
- InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen's kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.