ArticlePloS one2013
Automatic detection of whole night snoring events using non-contact microphone.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Non-contact diagnosis of obstructive sleep apnea using impulse-radio ultra-wideband radar.Scientific reports · 2020Trial
- Digital Technology for Sleep Symptoms in Parkinson's Disease: A Scoping Review.Journal of movement disorders · 2026Article
- High-Precision Contactless Stereo Acoustic Monitoring in Polysomnographic Studies of Children.Sensors (Basel, Switzerland) · 2025Article
- Automatic snoring detection using a hybrid 1D-2D convolutional neural network.Scientific reports · 2023Article
- Perceptual snoring as a basis for a psychoacoustical modeling and clinical patient profiling.Sleep & breathing = Schlaf & Atmung · 2022Article
- The acoustical and perceptual features of snore-related sounds in patients with obstructive sleep apnea sleeping with the dynamic mandibular advancement system MATRx plus®.Sleep & breathing = Schlaf & Atmung · 2022Article
- Validation of snoring detection using a smartphone app.Sleep & breathing = Schlaf & Atmung · 2022Article
- Screening Severe Obstructive Sleep Apnea in Children with Snoring.Diagnostics (Basel, Switzerland) · 2021Article
- Are annoyance scores based on sound pressure levels suitable for snoring assessment in the home environment?Sleep & breathing = Schlaf & Atmung · 2021Article
- Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis.BMC medical informatics and decision making · 2020Article
- A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis.International journal of environmental research and public health · 2020Article
- Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds.Frontiers in digital health · 2020Article
- Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.IEEE journal of biomedical and health informatics · 2019Article
- Sleep staging using nocturnal sound analysis.Scientific reports · 2018Article
- Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach.Scientific reports · 2018Article
- SNORAP: A Device for the Correction of Impaired Sleep Health by Using Tactile Stimulation for Individuals with Mild and Moderate Sleep Disordered Breathing.Sensors (Basel, Switzerland) · 2017Article
- Noncontact Sleep Study by Multi-Modal Sensor Fusion.Sensors (Basel, Switzerland) · 2017Article
- Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor.Journal of Korean medical science · 2017Article
- Review
- Snoring Sounds Predict Obstruction Sites and Surgical Response in Patients with Obstructive Sleep Apnea Hypopnea Syndrome.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAlthough awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology.
designSounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. PATIENTS: Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2), m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. MEASUREMENTS AND
resultsTo train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise).
conclusionsAudio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.
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