Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning

AI-driven approaches for automatic detection of sleep apnea/hypopnea based on human physiological signals: a review

Sleep apnea/hypopnea is a sleep disorder characterized by repeated pauses in breathing which could induce a series of health problems such as cardiovascular disease (CVD) and even sudden death. Polysomnography (PSG) is the most common way to diagnose sleep apnea/hypopnea. Considering that PSG data acquisition is complex and the diagnosis of sleep apnea/hypopnea requires manual scoring, it is very time-consuming and highly professional. With the development of wearable devices and AI techniques, more and more works have been focused on building machine and deep learning models that use single or multi-modal physiological signals to achieve automated detection of sleep apnea/hypopnea. This paper provides a comprehensive review of automatic sleep apnea/hypopnea detection methods based on AI-based techniques in recent years. We summarize the general process used by existing works with a flow chart, which mainly includes data acquisition, raw signal pre-processing, model construction, event classification, and evaluation, since few papers consider these. Additionally, the commonly used public database and pre-processing methods are also reviewed in this paper. After that, we separately summarize the existing methods related to different modal physiological signals including nasal airflow, pulse oxygen saturation (SpO2), electrocardiogram (ECG), electroencephalogram (EEG) and snoring sound. Furthermore, specific signal pre-processing methods based on the characteristics of different physiological signals are also covered. Finally, challenges need to be addressed, such as limited data availability, imbalanced data problem, multi-center study necessity etc., and future research directions related to AI are discussed.

Multi-modal multi-task deep neural networks for sleep disordered breathing assessment using cardiac and audio signals

BACKGROUND AND OBJECTIVE

Sleep disordered breathing (SDB) is one of the most common sleep disorders and has short-term consequences for daytime functioning while being a risk factor for several conditions, such as cardiovascular disease. Polysomnography, the current diagnostic gold standard, is expensive and has limited accessibility. Therefore, cost-effective and easily accessible methods for SDB detection are needed. Both cardiac and audio signals have received attention for SDB detection as they can be obtained with unobtrusive sensors, suitable for home applications.

METHODS

This paper introduces a multi-modal multi-task deep learning approach for SDB assessment using a combination of cardiac and audio signals under the assumption that they can provide complementary information. We aimed to estimate the apnea-hypopnea index (AHI) and assess AHI-based SDB severity through the detection of SDB events, combined with total sleep time estimated from simultaneous sleep-wake classification. Inter-beat interval and electrocardiogram-derived respiration from the electrocardiogram, and Mel-scale frequency cepstral coefficients from concurrent audio recordings were used as inputs. We compared the performance of several models trained with different combinations of these inputs.

RESULTS

Using cross-validation with a dataset comprising overnight recordings of 161 subjects, we achieved an F1 score of 0.588 for SDB event detection, a correlation coefficient of 0.825 for AHI estimation, and an accuracy of 57.8% for SDB severity classification (normal, mild, moderate, and severe).

CONCLUSION

Results show that combining cardiac and audio signals can enhance the performance of SDB detection and highlight the potential of multi-modal data fusion for further research in this domain.

Status and Opportunities of Machine Learning Applications in Obstructive Sleep Apnea: A Narrative Review

Background

Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms.

Objective

This narrative review evaluates the application of machine learning in OSA research, focusing on model performance, dataset characteristics, demographic representation, and validation strategies. We aim to identify trends and gaps to guide future research and improve clinical decision-making that leverages machine learning.

Methods

This narrative review examines data extracted from 254 scientific publications published in the PubMed database between January 2018 and March 2023. Studies were categorized by machine learning applications, models, tasks, validation metrics, data sources, and demographics.

Results

Our analysis revealed that most machine learning applications focused on OSA classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and wearable devices. We also found that study cohorts were predominantly overweight males, with an underrepresentation of women, younger obese adults, individuals over 60 years old, and diverse racial groups. Many studies had small sample sizes and limited use of robust model validation.

Conclusion

Our findings highlight the need for more inclusive research approaches, starting with adequate data collection in terms of sample size and bias mitigation for better generalizability of machine learning models in OSA research. Addressing these demographic gaps and methodological opportunities is critical for ensuring more robust and equitable applications of artificial intelligence in healthcare.

Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study

This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 2020, 60 adult habitual snorers underwent polysomnography while wearing a neck piezoelectric sensor that recorded snoring vibrations (70-250 Hz) and carotid artery pulsations (0.01-1.5 Hz). The initial dataset comprised 1167 silence, 1304 snoring, and 399 noise samples from 20 participants. Using a hybrid deep learning model comprising a one-dimensional convolutional neural network and gated-recurrent unit, the model identified snoring and apnea/hypopnea events, with sleep phases detected via pulse wave variability criteria. The model's efficacy in predicting severe SAS was assessed in the remaining 40 participants, achieving snoring detection rates of 0.88, 0.86, and 0.92, with respective loss rates of 0.39, 0.90, and 0.23. Classification accuracy for severe SAS improved from 0.85 for total sleep time to 0.90 for partial sleep time, excluding the first sleep phase, demonstrating precision of 0.84, recall of 1.00, and an F1 score of 0.91. This innovative approach of combining a hybrid deep learning model with a neck-wearable piezoelectric sensor suggests a promising route for early and precise differentiation of severe SAS from habitual snoring, aiding guiding further standard diagnostic evaluations and timely patient management. Future studies should focus on expanding the sample size, diversifying the patient population, and external validations in real-world settings to enhance the robustness and applicability of the findings.

A Dual-Modal Wearable Pulse Detection System Integrated with Deep Learning for High-Accuracy and Low-Power Sleep Apnea Monitoring

Despite being a serious health condition that significantly increases cardiovascular and metabolic disease risks, sleep apnea syndrome (SAS) remains largely underdiagnosed. While polysomnography (PSG) remains the gold standard for diagnosis, its clinical application is limited by high costs, complex setup requirements, and sleep quality interference. Although wearable devices using photoplethysmography (PPG) have shown promise in SAS detection, their continuous operation demands substantial power consumption, hindering long-term monitoring capabilities. Here, a dual-modal wearable system is presented integrating a piezoelectric nanogenerator (PENG) and PPG sensor with a biomimetic fingertip structure for SAS detection. A two-stage detection strategy is adopted where the self-powered PENG performs continuous preliminary screening, activating the PPG sensor only when suspicious events are detected. Combined with a Vision Transformer-based deep learning model, the high-accuracy configuration achieves 99.59% accuracy, while the low-power two-stage approach maintained 94.95% accuracy. This dual-modal wearable pulse detection system provides a practical solution for long-term SAS monitoring, overcoming the limitations of traditional PSG while maintaining high detection accuracy. The system's versatility in both home and clinical settings offers the potential for improving early detection rates and treatment outcomes for SAS patients.

Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation Using Recursive Spiking Neural Networks

OBJECTIVE

To develop a novel method for improved screening of sleep apnea in home environments, focusing on reliable estimation of the Apnea-Hypopnea Index (AHI) without the need for highly precise event localization.

METHODS

RSN-Count is introduced, a technique leveraging Spiking Neural Networks to directly count apneic events in recorded signals. This approach aims to reduce dependence on the exact time-based pinpointing of events, a potential source of variability in conventional analysis.

RESULTS

RSN-Count demonstrates a superior ability to quantify apneic events (AHI MAE ) compared to established methods (AHI MAE ) on a dataset of whole-night audio and SpO recordings (N = 33). This is particularly valuable for accurate AHI estimation, even in the absence of highly precise event localization.

CONCLUSION

RSN-Count offers a promising improvement in sleep apnea screening within home settings. Its focus on event quantification enhances AHI estimation accuracy.

SIGNIFICANCE

This method addresses limitations in current sleep apnea diagnostics, potentially increasing screening accuracy and accessibility while reducing dependence on costly and complex polysomnography.

Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics

Underpinned by rigorous clinical trial data, the use of existing home sleep apnoea testing is now commonly employed for sleep disordered breathing diagnostics in most clinical sleep centres globally. This has been a welcome addition for the field given the considerable burden of disease, cost, and access limitations with in-laboratory polysomnography testing. However, most existing home sleep apnoea testing approaches predominantly aim to replicate elements of conventional polysomnography in different forms with a focus on the estimation of the apnoea-hypopnoea index. New, simplified technology for sleep disordered breathing screening, detection/diagnosis, or monitoring has expanded exponentially in recent years. Emerging innovations in sleep monitoring technology now go beyond simple single-night replication of varying numbers of polysomnography signals in the home setting. These novel approaches have the potential to provide important new insights to overcome many of the existing limitations of sleep disordered breathing diagnostics and transform disease diagnosis and management to improve outcomes for patients. Accordingly, the current review summarises the existing evidence for sleep study testing in people with suspected sleep-related breathing disorders, discusses novel and emerging technologies and approaches according to three key categories: (1) wearables (e.g., body-worn sensors including wrist and finger sensors), (2) nearables (e.g., bed-embedded and bedside sensors), and (3) airables (e.g., audio and video recordings), and outlines their potential disruptive role to transform sleep disordered breathing diagnostics and care.

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

Importance

Consumer-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.

Objective

To 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 Participants

This 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 Measures

Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.

Results

Of 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 Relevance

This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach

Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems. An automatic detection system will allow for a high diagnosis rate and the analysis of additional patients. Deep learning (DL) is achieving high priority due to the availability of databases and recently developed methods. As the most up-and-coming technique for classification and generative tasks, DL has shown its significant potential in 2-dimensional clinical image processing studies. However, physiological information collected as 1-dimensional data has yet to be effectively extracted from this new approach to achieve the needed medical goals. So, in this study, we review the most recent studies in the field of DL applied to physiological data based on pulse oxygen saturation, electrocardiogram, airflow, and sound signal. A total of 47 articles from different journals and publishing houses that were published between 2012 and 2022 were identified. The primary objective of this work is to perform a comprehensive analysis to analyze, classify, and compare the main characteristics of deep-learning algorithms applied in physiological data processing for SLA detection. Overall, our analysis provides comprehensive and detailed information for researchers looking to add to this field. The data input source, objective, DL network, training framework, and database references are the critical factors of the DL approach examined. These are the most critical variables that influence system performance. We categorized the relevant research studies in physiological sensor data analysis using the DL approach based on (1) Physiological sensor data aspects, like signal types, sampling frequency, and window size; and (2) DL model perspectives, such as learning structure and input data types.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00297-5.

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

BACKGROUND

Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.

OBJECTIVE

This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population.

METHODS

We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.

RESULTS

We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.

CONCLUSIONS

These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.

TRIAL REGISTRATION

PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.