ReviewBiomedical engineering letters2019
Smart technologies toward sleep monitoring at home.
Review in Biomedical engineering letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
30 citing papers in PubMed, 89 citations in OpenAlex.
- Article
- A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability.Sleep and biological rhythms · 2025Review
- Sleep quality and efficiency in adults with post-acute sequelae of COVID-19.Sleep advances : a journal of the Sleep Research Society · 2025Article
- Accuracy Bias and Factors Influencing Polysomnography and Consumer Sleep-Monitoring Device Measuring of Total Sleep Time: A Mixed-Methods Study.Nature and science of sleep · 2025Article
- Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Article
- Association of Sleep Patterns and Respiratory Disturbance Index with Physiological Parameters in Pediatric Patients with Self-Perceived Short Stature.Diagnostics (Basel, Switzerland) · 2024Article
- Undersampling and cumulative class re-decision methods to improve detection of agitation in people with dementia.Biomedical engineering letters · 2024Article
- Sleep Quality and Perceived Stress among Health Science Students during Online Education-A Single Institution Study.Healthcare (Basel, Switzerland) · 2023Article
- A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring.Bioengineering (Basel, Switzerland) · 2023Article
- Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States.Children (Basel, Switzerland) · 2023Article
- Closed-Loop Auditory Stimulation to Guide Respiration: Preliminary Study to Evaluate the Effect on Time Spent in Sleep Initiation during a Nap.Sensors (Basel, Switzerland) · 2023Article
- Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study.Sensors (Basel, Switzerland) · 2023Article
- Wearable and digital devices to monitor and treat metabolic diseases.Nature metabolism · 2023Review
- Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors.Sensors (Basel, Switzerland) · 2022Article
- A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems.Polymers · 2022Article
- Article
- Detection of sleep apnea from single-channel electroencephalogram (EEG) using an explainable convolutional neural network (CNN).PloS one · 2022Article
- Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy.Frontiers in public health · 2022Article
- ENIGMA-Sleep: Challenges, opportunities, and the road map.Journal of sleep research · 2021Review
- [Comparison and correlation analysis of sleep parameters between watch-type sleep monitor (Actiwatch) and polysomnography].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea-hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.
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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.