ArticleSensors (Basel, Switzerland)2020
Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 46 citations in OpenAlex.
- Electrocardiographic Alterations in Obstructive Sleep Apnea Syndrome: Mechanisms and Clinical Relevance.Life (Basel, Switzerland) · 2026Review
- Introduction to electrocardiogram signal quality assessment and estimated accuracy for textile electrodes.Scientific reports · 2025Article
- The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea.Frontiers in cardiovascular medicine · 2025Article
- Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Article
- Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.Sleep medicine · 2024Article
- Recent Progress in Long-Term Sleep Monitoring Technology.Biosensors · 2023Review
- Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.Frontiers in neuroscience · 2023Article
- Article
- Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.Sensors (Basel, Switzerland) · 2022Article
- Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device.Sensors (Basel, Switzerland) · 2022Article
- Text mining-based measurement of precision of polysomnographic reports as basis for intervention.Journal of biomedical semantics · 2022Article
- A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals.Frontiers in neuroscience · 2022Article
- Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach.Nature and science of sleep · 2022Article
- At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch.Science advances · 2021Article
- Short-Term Wearable Sensors for In-Hospital Medical and Surgical Patients: Mixed Methods Analysis of Patient Perspectives.JMIR perioperative medicine · 2021Article
- Special Issue: ECG Monitoring System.Sensors (Basel, Switzerland) · 2021Article
- A Single-Center Validation of the Accuracy of a Photoplethysmography-Based Smartwatch for Screening Obstructive Sleep Apnea.Nature and science of sleep · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea-hypopnea events per hour of sleep (apnea-hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7-40] h - 1 . The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.
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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.