ArticleBiomedical engineering online2018
Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Detecting Sleep Apnea Using Deep Learning.Sensors (Basel, Switzerland) · 2019Pooled it
- Evaluation of a sleep sound analysis smartphone app for home obstructive sleep apnea screening among community-dwelling Chinese adults with hypertension.Sleep & breathing = Schlaf & Atmung · 2026Trial
- Utilisation of machine learning to predict surgical candidates for the treatment of childhood upper airway obstruction.Sleep & breathing = Schlaf & Atmung · 2022Trial
- Smartphone-Based Acoustic Sensing for Breathing and Heartbeat Detection via AoA Clustering in Indoor Environments.Sensors (Basel, Switzerland) · 2026Article
- Early Screening of Sleep-Disordered Breathing Using Metaheuristic-Optimized Extreme Learning Machines.Diagnostics (Basel, Switzerland) · 2026Article
- Non-contact on-device detection of obstructive sleep apnea from infrared video.Nature communications · 2026Article
- Interpretable Acoustic Features from Wakefulness Tracheal Breathing for OSA Severity Assessment.Journal of clinical medicine · 2026Article
- Validation of RBDtector: An Open-Source Automated Software for Scoring REM Sleep Without Atonia.Journal of sleep research · 2025Article
- Optimizing MFCC Parameters for Breathing Phase Detection.Sensors (Basel, Switzerland) · 2025Article
- Exploring the Relationship Between Obstructive Sleep Apnea and Olfactory Function.Life (Basel, Switzerland) · 2025Review
- Construction and validation of a predictive model for sleep disorders among pregnant women.BMC pregnancy and childbirth · 2025Article
- Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.Journal of thoracic disease · 2024Review
- Identification of OSAHS patients based on ReliefF-mRMR feature selection.Physical and engineering sciences in medicine · 2024Article
- Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.Sensors (Basel, Switzerland) · 2023Article
- Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.Biomedical engineering letters · 2023Review
- Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2023Article
- Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.Diagnostics (Basel, Switzerland) · 2023Review
- RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria.Scientific reports · 2022Article
- A Review of Automated Bioacoustics and General Acoustics Classification Research.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeBreathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing the breathing sounds collected from a large number of subjects during entire overnight sleep.
methodsThe participants were patients who presented at a sleep center with snoring or cessation of breathing during sleep. They were subjected to full-night polysomnography (PSG) during which the breathing sound was recorded using a microphone. Then, audio features were extracted and a group of features differing significantly between different SDB severity groups was selected as a potential acoustic biomarker. To assess the validity of the acoustic biomarker, classification tasks were performed using several machine learning techniques. Based on the apnea-hypopnea index of the subjects, four-group classification and binary classification were performed.
resultsUsing tenfold cross validation, we achieved an accuracy of 88.3% in the four-group classification and an accuracy of 92.5% in the binary classification. Experimental evaluation demonstrated that the models trained on the proposed acoustic biomarkers can be used to estimate the severity of SDB.
conclusionsAcoustic biomarkers may be useful to accurately predict the severity of SDB based on the patient's breathing sounds during sleep, without conducting attended full-night PSG. This study implies that any device with a microphone, such as a smartphone, could be potentially utilized outside specialized facilities as a screening tool for detecting SDB.
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