Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Romero, Hector E.; Ma, Ning; Brown, Guy J.; Hill, Elizabeth A.

doi:10.1109/jbhi.2022.3154719

Cited by 23 publications

(16 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study is also one of the pioneering studies in collecting real-world data rather than relying on in-laboratory data sets. Previously, to our knowledge, only 1 study used smartphone-recorded sounds in a home setting, referencing level 3 HSAT (SOMNOtouch RESP [SOMNOmedics GmbH]) . Therefore, no electroencephalogram was incorporated, potentially resulting in an underestimation of AHI.…”

Section: Discussionmentioning

confidence: 99%

“…Typically implemented as applications, these devices can be easily installed on personal mobile devices, ensuring convenient access for patients. Some studies focus on tracheal sounds, while others use speech or ambient sound . However, many of these studies conducted sound recordings in controlled, noise-free environments, which do not accurately represent the typical background noises experienced by patients in their home sleep settings.…”

Section: Discussionmentioning

confidence: 99%

“…Sound-based assessment for OSA has emerged as a promising approach that enables noncontact and in-home monitoring using smartphones or wearable devices . Previous studies have demonstrated the potential of using breathing sounds to develop predictive models for OSA detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

Han,

Kim,

Rhee

et al. 2024

JAMA Otolaryngol Head Neck Surg

View full text Add to dashboard Cite

ImportanceConsumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.ObjectiveTo validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.Design, Setting, and ParticipantsThis diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants’ own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.Main Outcomes and MeasuresSensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.ResultsOf the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.Conclusions and RelevanceThis diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

Han,

Kim,

Rhee

et al. 2024

JAMA Otolaryngol Head Neck Surg

View full text Add to dashboard Cite

show abstract

“…Additional variability of the signal quality was detected before the true sleeping phase and because of awaking in the early morning. It is well documented actually that polysomnography in general is especially prone to the so-called ‘first-night effect’, in which the first night of experiment shows more sleep fragmentation, longer initial sleep delay, less total sleep time, and more wakefulness when compared to subsequent nights [ 58 ]. In our specific case, we decided a posteriori to retain the central part of the night (about 2.5 h) that showed enough signal stability for all the 5 subjects.…”

Section: Discussionmentioning

confidence: 99%

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks

Rossi

Alessandrelli

Dombrovschi

et al. 2022

Sensors

View full text Add to dashboard Cite

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.

show abstract

“…However, a more natural and correct way to diagnose OSA is to detect and count individual apneic and hypopneic events. Only a few recent studies have tried to detect apnea events by observing the recovery breath or loud gasp after an apnea event [5]. Deeper investigations into OSA event detection under various and severe home environment noises are needed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

Le¹,

Kim²,

Cho³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. Objective The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. Methods This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as “apnea,” “hypopnea,” or “no-event,” and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). Results Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for “no-event,” 84% for “apnea,” and 51% for “hypopnea.” Most misclassifications were made for “hypopnea,” with 15% and 34% of “hypopnea” being wrongly predicted as “apnea” and “no-event,” respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. Conclusions Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

show abstract

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Cited by 23 publications

References 43 publications

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

Contact Info

Product

Resources

About