ArticlePloS one2015
Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluating sleep quality in a non-intrusive manner using contactless ballistocardiography and audio signals through a LSTM-TCN machine learning model.Frontiers in network physiology · 2026Article
- High-Precision Contactless Stereo Acoustic Monitoring in Polysomnographic Studies of Children.Sensors (Basel, Switzerland) · 2025Article
- Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis.Scientific reports · 2024Article
- Alternatives to Polysomnography for the Diagnosis of Pediatric Obstructive Sleep Apnea.Diagnostics (Basel, Switzerland) · 2023Review
- Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data.Sensors (Basel, Switzerland) · 2021Article
- Contact-free sensor signals as a new digital biomarker for cardiovascular disease: chances and challenges.European heart journal. Digital health · 2020Review
- Sleep/Wakefulness Detection Using Tracheal Sounds and Movements.Nature and science of sleep · 2020Article
- Sleep stage classification from heart-rate variability using long short-term memory neural networks.Scientific reports · 2019Article
- 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
- Sleep staging using nocturnal sound analysis.Scientific reports · 2018Article
- Noncontact Sleep Study by Multi-Modal Sensor Fusion.Sensors (Basel, Switzerland) · 2017Article
- Breathing and Snoring Sound Characteristics during Sleep in Adults.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesTo develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA).
designWhole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity.
settingUniversity affiliated sleep-wake disorder center and biomedical signal processing laboratory. PATIENTS: One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects. MEASUREMENTS AND
resultsEpoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively.
conclusionsThis study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances.
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