ArticleJournal of Korean medical science2017
Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor.
Article in Journal of Korean medical science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- Clinical study of an integrated sensor system for detection and classification of obstructive sleep apnea (OSA).Scientific reports · 2025Article
- Human-Centered Sensor Technologies for Soft Robotic Grippers: A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea.BMC neuroscience · 2024Article
- International Consensus Statement on Obstructive Sleep Apnea.International forum of allergy & rhinology · 2023Review
- Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model.Computational intelligence and neuroscience · 2021Article
- White matter alteration and autonomic impairment in obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020Article
- Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2019Article
- Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset.Clinical and experimental otorhinolaryngology · 2019Article
- Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.Journal of medical systems · 2018Article
- Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.
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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.