ArticleNPJ digital medicine2019
Benchmark on a large cohort for sleep-wake classification with machine learning techniques.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring the Relationship Between General Motor Activity and Optimal Actigraphy Sleep Configurations: A Systematic Review.Journal of sleep research · 2026Pooled it
- A Systematic Review of Sensing Technologies for Wearable Sleep Staging.Sensors (Basel, Switzerland) · 2021Pooled it
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- Universal approach to actigraphic sleep/wake scoring, verified against 5 classic algorithms on 3 datasets.Scientific reports · 2026Article
- Hybrid Deep Learning Framework for Sleep Quality Prediction: Integrating Metaheuristic Optimization and Statistical Features.Brain and behavior · 2026Article
- Sleep awake detection from leg-worn wearables using deep sensor fusion.Scientific reports · 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
- Actigraphy-based sleep outcomes in substance use disorders: A protocol for a systematic review and meta-analysis.PloS one · 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
- Comparison and Validation of Actigraphy Algorithms Using a Large Community Dataset: Algorithm Validation Study.JMIR formative research · 2025Article
- Actigraphy against 32-hour polysomnography in patients with suspected idiopathic hypersomnia.Journal of sleep research · 2025Article
- Article
- A novel feature extractor based on constrained cross network for detecting sleep state.Scientific reports · 2025Article
- Variability of the Skin Temperature from Wrist-Worn Device for Definition of Novel Digital Biomarkers of Glycemia.Sensors (Basel, Switzerland) · 2025Article
- Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- WACSAW: An adaptive, statistical method to classify movement into sleep and wakefulness states.PloS one · 2025Article
- Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study.PloS one · 2025Article
- Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and
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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.