ArticleMedical & biological engineering & computing2012
ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern.
Article in Medical & biological engineering & computing, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Automatic sleep staging by cardiorespiratory signals: a systematic review.Sleep & breathing = Schlaf & Atmung · 2022Pooled it
- Review
- Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection.IEEE open journal of engineering in medicine and biology · 2024Article
- Review
- Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier.Entropy (Basel, Switzerland) · 2023Article
- Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device.Sensors (Basel, Switzerland) · 2022Article
- Development and Test of a Portable ECG Device with Dry Capacitive Electrodes and Driven Right Leg Circuit.Sensors (Basel, Switzerland) · 2021Article
- An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.Sensors (Basel, Switzerland) · 2018Article
- Assessment of respiratory flow cycle morphology in patients with chronic heart failure.Medical & biological engineering & computing · 2017Article
- Heart Rate Variability Monitoring during Sleep Based on Capacitively Coupled Textile Electrodes on a Bed.Sensors (Basel, Switzerland) · 2015Article
- All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome.Medical & biological engineering & computing · 2012Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases.
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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.