Sleep-quality assessment from full night audio recordings of sleep apnea patients

Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

doi:10.1109/embc.2012.6346760

Cited by 20 publications

(15 citation statements)

References 11 publications

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“…The first selected feature extracts the apnea events in the audio signal and calculates the average number of apneas per hour -which is actually the definition of AHI. The sleep/wake detector [15] was an essential pre-step for this feature calculation and for some others. The second selected feature calculates the variance of the snores skewness based on the energy signal -which is a measure for inter-snore homogeneity -strengthening the theory that OSA snorers tend to have a greater variance of snores.…”

Section: Discussionmentioning

confidence: 99%

“…1. Our previously published snore detector [14] and sleep/wake detector [15] were used as the first steps of each phase. Once the snores are detected, an analysis of all snore events across the night is performed; various acoustic features are extracted and investigated.…”

Section: Methodsmentioning

confidence: 99%

“…An additional module used in this study is the sleep quality estimation system [15]; this system automatically detects sleep/wake epochs from audio signal and calculates sleep quality parameters such as total sleep time. The overall accuracy detection of our proposed system for sleep/wake decision is about 82%.…”

Section: Sleep/wake Detectormentioning

confidence: 99%

See 2 more Smart Citations

OSA severity assessment based on sleep breathing analysis using ambient microphone

Dafna

Tarasiuk

Zigel

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this paper, an audio-based system for severity estimation of obstructive sleep apnea (OSA) is proposed. The system estimates the apnea-hypopnea index (AHI), which is the average number of apneic events per hour of sleep. This system is based on a Gaussian mixture regression algorithm that was trained and validated on full-night audio recordings. Feature selection process using a genetic algorithm was applied to select the best features extracted from time and spectra domains. A total of 155 subjects, referred to in-laboratory polysomnography (PSG) study, were recruited. Using the PSG's AHI score as a gold-standard, the performances of the proposed system were evaluated using a Pearson correlation, AHI error, and diagnostic agreement methods. Correlation of R=0.89, AHI error of 7.35 events/hr, and diagnostic agreement of 77.3% were achieved, showing encouraging performances and a reliable non-contact alternative method for OSA severity estimation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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OSA severity assessment based on sleep breathing analysis using ambient microphone

Dafna

Tarasiuk

Zigel

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…Current sleep apnoea audio recording systems acquire the signal with a broad range of sampling rates (5 kHz [28], 20 kHz [29], 44.1 kHz [30]). OSA patients' snoring sounds present a wide range of frequency response spectra depending on the physical phenomenon causing it.…”

Section: Operating System and General Set-upmentioning

confidence: 99%

SleepAp: An Automated Obstructive Sleep Apnoea Screening Application for Smartphones

Behar

Roebuck

Shahid

et al. 2015

IEEE J. Biomed. Health Inform.

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Obstructive sleep apnoea (OSA) is a sleep disorder with long-term consequences. Long-term effects include sleep-related issues and cardiovascular diseases. OSA is often diagnosed with an overnight sleep test called a polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper, a novel OSA screening framework and prototype phone application are introduced. A database of 856 patients that underwent at-home polygraphy was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG), and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients and tested on 121 patients. Classification on the test set had an accuracy of up to 92.2% when classifying subjects as having moderate or severe OSA versus being healthy or a snorer based on the clinicians' diagnoses. The signal processing and machine learning algorithms were ported to Java and integrated into the phone application-SleepAp. SleepAp records the body position, audio, actigraphy and PPG signals, and implements the clinically validated STOP-BANG questionnaire. It derives features from the signals and classifies the user as having OSA or not using the SVM trained on the clinical database. The resulting software could provide a new, easy-to-use, low-cost, and widely available modality for OSA screening.

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“…Moreover, several studies have found that expiratory events may hold hidden information for SDB diagnosis [9][10][11]. Thus, by using the additional expiratory information, SDB diagnosis and sleep quality assessment can be improved [12,13].…”

Section: Introductionmentioning

confidence: 98%

Detection of breathing sounds during sleep using non-contact audio recordings

Rosenwein

Dafna

Tarasiuk

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Evaluation of respiratory activity during sleep is essential in order to reliably diagnose sleep disorder breathing (SDB); a condition associated with serious cardio-vascular morbidity and mortality. In the current study, we developed and validated a robust automatic breathing-sounds (i.e. inspiratory and expiratory sounds) detection system of audio signals acquired during sleep. Random forest classifier was trained and tested using inspiratory/expiratory/noise events (episodes), acquired from 84 subjects consecutively and prospectively referred to SDB diagnosis in sleep laboratory and in at-home environment. More than 560,000 events were analyzed, including a variety of recording devices and different environments. The system's overall accuracy rate is 88.8%, with accuracy rate of 91.2% and 83.6% in in-laboratory and at-home environments respectively, when classifying between inspiratory, expiratory, and noise classes. Here, we provide evidence that breathing-sounds can be reliably detected using non-contact audio technology in at-home environment. The proposed approach may improve our understanding of respiratory activity during sleep. This in return, will improve early SDB diagnosis and treatment.

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Sleep-quality assessment from full night audio recordings of sleep apnea patients

Cited by 20 publications

References 11 publications

OSA severity assessment based on sleep breathing analysis using ambient microphone

OSA severity assessment based on sleep breathing analysis using ambient microphone

SleepAp: An Automated Obstructive Sleep Apnoea Screening Application for Smartphones

Detection of breathing sounds during sleep using non-contact audio recordings

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