Sleep Apnea Detection Research Articles

Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful toolto screen for OSA. We recruited consecutivesubjects suspected of OSA who had received sleep examination and photographing. Sixty-eight points from 2-dimensional facial photos were labelled by automated identification. An optimized model with facial features and basic clinical information was established and tenfold cross-validation was performed. Area under the receiver operating characteristic curve (AUC) indicated the model's performance using sleep monitoring as the reference standard. A total of 653 subjects (77.2% males, 55.3% OSA) were analyzed. CATBOOST was the most suitable algorithm for OSA classification with a sensitivity, specificity, accuracy, and AUC of 0.75, 0.66, 0.71, and 0.76 respectively (P < 0.05), which was better than STOP-Bang questionnaire, NoSAS scores, and Epworth scale. Witnessed apnea by sleep partner was the most powerful variable, followed by body mass index, neck circumference, facial parameters, and hypertension. The model's performance became more robust with a sensitivity of 0.94, for patients with frequent supine sleep apnea. Thefindings suggest that craniofacial features extracted from 2-dimensional frontal photos, especially in the mandibular segment, have the potential to become predictors of OSA in the Chinese population. Machine learning-derived automatic recognition may facilitate the self-help screening for OSA in a quick, radiation-free, and repeatable manner.

0557 Sleep apnea detection using photoplethysmography using wearable electronic devices : A systematic review and meta-analysis

Abstract Introduction Sleep apnea, a hyper common disorder is estimated to affect almost 1 billion people with the prevalence of more than 50% in some countries. It is almost impossible to do gold standard polysomnography for all the population for screening. Multiple studies have reported the use of cost effective wearable devices using photoplethysmography (PPG) for the screening. To our knowledge, we presented the first meta-analysis of all studies looking at PPG and screening of sleep apnea. Methods We conducted a systematic review and meta-analysis under the PRISMA guidelines. Study Eligibility criteria included patients aged &amp;gt; 18 without a prior diagnosis of sleep disorder. The intervention included screening using a wearable device using PPG with comparison using standard of care sleep study (e.g polysomnography). The databases used were Cochrane, PubMed, Embase and Google scholar. Search strategy with Boolean logic included Sleep apnea, wearable device, smart, photoplethysmography, PPG, smart and smartwatch. Outcomes were pooled diagnostic odds ratio, meta-analyzed area under the curve (AUC),, and forest plots of sensitivity &amp; specificity. Statistical analysis was done using the programming language R with R package mada. Fixed and random effects models were used to derive diagnostic odds ratio (DOR) and paired forest plots for sensitivity &amp; specificity. Summary Receiver operation curve was made using proportional hazard model and Reitsma et al model with a bivariate approach. Results The search strategy revealed following number of studies: PubMed (270), Cochrane (0), Embase (41), and Google Scholar(728). Total of 12 studies were included. We found high DOR with the fixed effect model which was 28.979. Mean DOR with the random effects model was 27.6. Bivariate diagnostic random-effects meta-analyzed AUC was 0.902. Studies used various electronic devices some of which are commercially available such as E4 Wristband, Galaxy watch 4, Smartwatch GT2, Belun ring etc. 4 studies used PPG solely while others used multimodal data. 7 studies used conventional machine learning algorithm for analysis. Conclusion Screening of sleep apnea could be done in a cost-effective manner from commercially available devices using photoplethysmography owing to possible high sensitivity, AUC and DOR. More studies for data replicability and further standardization is needed. Support (if any)

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Validation of a transthoracic impendance sensor for screening of sleep apnea in CIED patients: a pooled analysis of AIRLESS and UPGRADE

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Scientific grant support from the Boston Scientific Investigator Sponsored Research (ISR) Committee, Boston Scientific, St. Paul, MN, USA. Background/Introduction Cardiac implantable electronic devices and their integrated thoracic impedance sensors, meant to detect sleep apnea, are available for over a decade now. Despite its usage in daily clinical practice, there is only limited data on its diagnostic accuracy. Purpose To further validate an apnea sensor by using polysomnography (PSG) and investigate subgroups with potential differences in diagnostic accuracy. Methods AIRLESS and UPGRADE were prospective investigator driven trials meant to validate an apnea sensor. Patients, who either fulfilled the criteria for implantation of an implantable cardioverter-defibrillator (ICD), cardiac resynchronization therapy (CRT) or upgrading to CRT according to current guidelines were eligible for enrolment. Sleep apnea and its severity, measured by apnea-hypopnea index (AHI), were exclusively assessed by PSG. PSG was conducted in a time interval of 1 to 3 months post implantation of the device. For direct comparison, the apnea sensor derived respiratory disturbance index (RDI) was used from the identical night. Bivariate correlations (spearman coefficient) as well as the Bland-Altmann method were applied to assess diagnostic accuracy of the apnea sensor. Results Overall, 80 patients were analysed, out of whom the majority (n = 35, 43.8%) were diagnosed with central sleep apnea. Sixteen (20.0%) patients received a conventional ICD system, whereas 21 (26.3%) were implanted a CRT-P and 43 (53.8%) a CRT-D. Atrial fibrillation was known in 50.0% (n=40) of patients, 45 patients suffered from periodic leg movement syndrome (PLMS, 56.3%). In the overall cohort, the sensor derived RDI correlated significantly with the AHI (r = 0.61, p &amp;lt; 0.001) with a mean difference (MD) of -12.6 (95% CI -38.2 – 13.0). The sensor did specifically perform well in patients without atrial fibrillation (r = 0.80, p &amp;lt; 0.001; MD -9.0, 95% CI -30.4 – 12.5), while the presence of PLMS did not affect the accuracy of measurements (PLMS: r = 0.60, p = 0.007, MD -16.1, 95% CI -43.9 – 11.8 vs. no PLMS: r = 0.62, p = 0.001, MD -9.9, 95% CI -32.8 – 12.9). Furthermore, the RDI was found to correlate well in patients with central sleep apnea (r = 0.61, p = 0.013), although similar overestimation was observed (MD -11.6, 95% CI -35.4 – 12.2) as in other subgroups. Conclusion In an exclusive heart failure cohort, the apnea sensor did correlate well with the PSG derived AHI. Although similar correlation is found in most subgroups, there remains to be a decent overestimation of events.

Usefulness of sleep apnea monitoring by pacemaker sensor in elderly patients with diastolic dysfunction

BackgroundAutomated detection of sleep apnea (SA) by pacemaker (PM) has been proposed and exhibited good agreement with polysomnography to detect severe SA. We aimed to evaluate the usefulness of SA monitoring algorithm in elderly patients with diastolic dysfunction. MethodsConsecutive patients referred to the Caen University Hospital for PM implantation between May 2016 and December 2018 presenting isolated diastolic dysfunction were eligible for the study. The respiratory disturbance index (RDI) measured by the PM, and the mean monthly RDI (RDIm), were compared to the apnea hypopnea index (AHI) assessed with portable monitor for severe SA diagnosis. ResultsDuring the study period, 68 patients were recruited, aged of 80.4 ± 8.2 years. 63 patients underwent polygraphy with a portable monitor: 57 presented SA (83.8%), including 16 with severe SA (23.5%). Eight were treated with continuous positive airway pressure (CPAP). We found the RDI cutoff value of 22 events/h to predict severe SA, with 71.4% sensitivity and 65.2%, specificity. The RDIm cutoff value to detect severe SA was 19 events/h, with a sensitivity of 60% and a specificity of 66%. There was a significant reduction in RDI (p = 0.041), RDIm (p = 0.039) and AHI (p = 0.002) after CPAP. Supraventricular arrhythmias were frequent in all patients, regardless of SA severity, considering either episodes occurrence or total burden. ConclusionIn a population of elderly patients with PM and diastolic dysfunction, the SA monitoring algorithm was able to detect severe SA, with good diagnostic performance values, but also to provide follow-up data for the patients treated with CPAP.

Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea

An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.

Sleep Apnea Detection by Tracheal Motion and Sound, and Oximetry via Application of Deep Neural Networks.

Sleep apnea (SA) is highly prevalent, but under diagnosed due to inaccessibility of sleep testing. To address this issue, portable devices for home sleep testing have been developed to provide convenience with reasonable accuracy in diagnosing SA. The objective of this study was to test the validity a novel portable sleep apnea testing device, BresoDX1, in SA diagnosis, via recording of trachea-sternal motion, tracheal sound and oximetry. Adults with a suspected sleep disorder were recruited to undergo in-laboratory polysomnography (PSG) and a simultaneous BresoDX1 recording. Data from BresoDX1 were collected and features related to breathing sounds, body motions and oximetry were extracted. A deep neural network (DNN) model was trained with 61-second epochs of the extracted features to detect apneas and hypopneas from which an apnea-hypopnea index (AHI) was calculated. The AHI estimated by BresoDX1 (AHIbreso) was compared to the AHI determined from PSG (AHIPSG) and the sensitivity and specificity of SA diagnosis were assessed at an AHIPSG ≥ 15. Two-hundred thirty-three participants (mean ± SD) 50 ± 16 years of age, with BMI of 29.8 ± 6.6 and AHI of 19.5 ± 22.7, were included. There was a strong relationship between AHIbreso and AHIPSG (r = 0.91, p < 0.001). SA detection for an AHIPSG ≥ 15 was highly sensitive (90.0%) and specific (85.9%). We conclude that the DNN model we developed via recording and analyses of trachea-sternal motion and sound along with oximetry provides an accurate estimate of the AHIPSG with high sensitivity and specificity. Therefore, BresoDX1 is a simple, convenient and accurate portable SA monitoring device that could be employed for home SA testing in the future.

Obstructive sleep apnea detection with nonlinear analysis of speech

Studies on the detection of Obstructive Sleep Apnea (OSA) from speech recording have brought an important innovation to this field. This study is based on the hypothesis that the deterioration of the muscles and tissues in the vocal tract in OSA patients changes the nonlinear properties of voice. In this study, the features that reveal the nonlinear structure of the speech for OSA detection were investigated. The nonlinear characteristics were evaluated for vowels and consonants, and it was tried to find out in which voice group the nonlinear features were more distinctive in OSA. The nonlinear analysis was applied to a wide variety of voice samples having vocal tract components affected by OSA, and OSA/healthy classifications were realized. The results revealed that nonlinear analysis gives considerable findings in OSA detection, and consonants are more successful than vowels. For classifications, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers, 5-fold cross validation procedure and Minimum Redundancy Maximum Relevance (MRMR) feature selection method were used. Using the whole dataset, OSA detection performance for vowels was found as 83.5% with KNN, and 96% with SVM for consonants. Additionally, the test process was carried out by using a distinct group, and 82.5% test accuracy was achieved with only 6 features for consonants. The results indicated that the proposed study supports the hypothesis that the nonlinear behavior of vocal tract changes in subjects with OSA, especially for consonants, and has considerable potential for OSA detection as the pre-diagnosis or screening test in clinical use.

Integrating medical rules to assist attention for sleep apnea detection

AbstractSleep apnea is one of the most common sleep disorders. The consequences of undiagnosed sleep apnea can be very serious, increasing the risk of high blood pressure, heart disease, stroke, and Alzheimer’s disease over a long period of time. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is nighttime polysomnography monitoring in a specialized sleep laboratory. However, these diagnoses are expensive and the number of beds is limited, and there is insufficient monitoring in terms of time dimension. Existing methods for automated detection use no more than three physiological signals, but all other signals are also associated with the patient’s sleep. In addition, the limited amount of medical real annotation data, especially abnormal samples, lead to weak model generalization capability. The gap between model generalization capability and medical field needs still exists. In this paper, we propose a method for integrating medical interpretation rules into a long short-term memory neural network based on self-attention with multichannel respiratory signals as input. We obtain attention weights through a token-level attention mechanism and then extract key rules of medical interpretation to assist the weights, improving model generalization and reducing the dependence on data volume. Compared with the best prediction performance of existing methods, the average improvements of our method in accuracy, precision, and f1-score are 3.26%, 7.03%, and 1.78%, respectively. The algorithm tested the performance of our model on the Sleep Heart Health Study data set and found that the model outperformed existing methods and could help physicians make decisions in their practices.

Self-x based closed loop wearable IoT for real-time detection and resolution of sleep apnea

This study introduces the Sleepcare Kit and S-Pillow (SKP), a wearable body-area network concept designed to address the uncomfortable and imprecise measurements associated with previous sleep apnea diagnosis and treatment methods that depend on external servers or human experts. SKP measures respiration, acceleration, and SpO2 signals to accurately identify risky sleep apnea in real-time. In addition, it incorporates a S-Pillow that can deterministically adjust the position of the patient’s head to resolve sleep apnea symptoms. SKP operates in a self-x based closed loop to optimally manage sleep apnea risk in a patient’s home environment without external help. Experimental results shows a 1/6 reduction in control frequency and a 1/3 reduction in apnea time compared to previous studies related to pillow use.

Analysis of Abdominal Respiratory Sensor Performance in Sleep Apnea Conditions

Abdominal respiratory sensor is a sensor used to detect sleep apnea that is specifically for neonates, this sensor is specifically for neonates because the use of this sensor does not require a voltage input to activate the sensor. In the absence of voltage input so as not to disturb the heart rhythm in neonates. When the sensor is no longer elastic, the pressure difference generated by the sensor will be unstable so that the sensor cannot work optimally. With these conditions, the period of use of the sensor needs to be known how durable the sensor is when it is used on patients so that the sensor can maximally detect the occurrence of apnea in neonates. How many times have you been in apnea. This study uses an Arduino microcontroller to process the pressure value and RR value generated by the stomach sensor and the MPX5010dp pressure sensor. the research method used is to use a simulator to analyze the combination of abdominal sensors and pressure sensors to monitor apnea. If viewed based on the average error, the error value in the RR 10bpm setting is ±0.185%, the RR 15 setting is ±0.245%, and the setting RR 20bpm is ±0.383%. From the average error value, it can be said that the higher the RR setting value, the higher the average error for each decrease in pressure output. it can be concluded that the performance of the use of the Abdominal Respiratory Sensor and Pressure Sensor on the Apnea Monitoring module functions well in detecting RR according to the settings on the simulator for 3-day monitoring. The development that can be done in this research is to use a more sensitive pressure sensor so that the results obtained are more stable and make the module display more attractive.

RAFNet: Restricted attention fusion network for sleep apnea detection

Sleep apnea (SA) is a common sleep-related breathing disorder, which would lead to damage of multiple systemic organs or even sudden death. In clinical practice, portable device is an important tool to monitor sleep conditions and detect SA events by using physiological signals. However, SA detection performance is still limited due to physiological signals with time-variability and complexity. In this paper, we focus on SA detection with single lead ECG signals, which can be easily collected by a portable device. Under this context, we propose a restricted attention fusion network called RAFNet for sleep apnea detection. Specifically, RR intervals (RRI) and R-peak amplitudes (Rpeak) are generated from ECG signals and divided into one-minute-long segments. To alleviate the problem of insufficient feature information of the target segment, we combine the target segment with two pre- and post-adjacent segments in sequence, (i.e. a five-minute-long segment), as the input. Meanwhile, by leveraging the target segment as the query vector, we propose a new restricted attention mechanism with cascaded morphological and temporal attentions, which can effectively learn the feature information and depress redundant feature information from the adjacent segments with adaptive assigning weight importance. To further improve the SA detection performance, the target and adjacent segment features are fused together with the channel-wise stacking scheme. Experiment results on the public Apnea-ECG dataset and the real clinical FAH-ECG dataset with sleep apnea annotations show that the RAFNet greatly improves SA detection performance and achieves competitive results, which are superior to those achieved by the state-of-the-art baselines.

A deep learning model developed for sleep apnea detection: A multi-center study

Objective. Manual detection of obstructive sleep apnea (OSA) is a challenging task for many reasons such as time-consuming and subjective. This study aimed to develop a deep learning model for automatically detecting abnormal sleep respiratory episodes in patients with OSA and to explore the performance differences between different sleep centers and different kind of sleep monitoring devices. Methods. The proposed model which comprised branch convolution, separable convolution, and self-attention mechanisms, took minute-to-minute raw electroencephalography (EEG) and oral-nasal airflow signals as its input. Results. The results of five-fold cross-validation in 50 clinical polysomnography recordings evidenced that the average accuracy was 0.877, with a sensitivity and specificity of 0.837, and 0.873, respectively. The proposed method achieved almost perfect agreement with manual scoring method according to the apnea-hypopnea index. In addition, experiments revealed that the combination of EEG and oral-nasal airflow signals could improve the accuracy of approximately 10% relative to oral-nasal airflow signals. Furthermore, a significant difference (P < 0.05) in performance between different sleep centers and different sleep-monitoring devices was also observed. Conclusion and significance. The proposed model achieved a comparably high performance in three different sleep centers, indicating its feasibility and robustness. This study suggested the reliable and simple alternative to clinical OSA screening. The model and data can be found at https://github.com/Lifanfan-dlut/OSA-detection.

The case for improving the detection and treatment of obstructive sleep apnea following stroke.

[See related research at www.cmaj.ca/lookup/doi/10.1503/cmaj.221063][1] KEY POINTS Stroke is a leading cause of death and disability for people in Canada. Beyond the initial brain injury, the sequelae of stroke may also include several comorbidities, with sleep disorders being among the most

ApneaNet: A hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals

Obstructive Sleep Apnea is a respiratory disorder that can be the origin of fatal heart and neurological health concerns if left untreated. Despite the availability of diagnosis methods, it is still undiagnosed in most cases due to the tiresome and impractical process of Polysomnography, the current medical standard test. In this study, the authors have worked towards finding a viable approach for easy and early diagnosis of sleep apnea using Electrocardiogram signals. The first model of this work was adapted from the Alexnet architecture with modifications done according to the input digitized signals. A Long–Short term memory layer was added to take care of temporal dependency in the dataset. It has shown an accuracy of 90.87%, specificity of 83.43%, and sensitivity of 95.48%. The hybrid architecture has 1.7 million parameters, much less than the Traditional Alexnet architecture. The second model, ApneaNet, has been introduced, which shows remarkable performance with an accuracy of 90.13%, specificity of 82.06%, and sensitivity of 95.14%, using only 0.9 million parameters which reduce the computational power significantly. The Proposed models have been implemented on a dataset split into 35 recordings for training and testing, showing a trailblazing accuracy of 95.69% and 96.37%, respectively. The authors proposed two deep learning models to detect sleep apnea events using Electrocardiography signals which have demonstrated competitive results compared to the state-of-the-art models at a low computational cost. We believe these methods have the potential to be successfully and efficiently used for the real-time detection of Sleep Apnea.

A clinical evaluation of a low-cost strain gauge respiration belt and machine learning to detect sleep apnea

Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via polysomnography which is an expensive test conducted in a sleep lab requiring sleep experts to manually score the recorded data. Since the symptoms of sleep apnea are often ambiguous, it is difficult for a physician to decide whether to prescribe polysomnography. In this study, we investigate whether helpful information can be obtained by collecting and automatically analysing sleep data using a smartphone and an inexpensive strain gauge respiration belt. We evaluate how accurately we can detect sleep apnea with wide variety of machine learning techniques with data from a clinical study with 49 overnight sleep recordings. With less than one hour of training, we can distinguish between normal and apneic minutes with an accuracy, sensitivity, and specificity of 0.7609, 0.7833, and 0.7217, respectively. These results can be achieved even if we train only on high-quality data from an entirely separate, clinically certified sensor, which has the potential to substantially reduce the cost of data collection. Data from a complete night can be analyzed in about one second on a smartphone.

Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier.

Obstructive Sleep Apnea (OSA) is a common sleep-breathing disorder that highly reduces the quality of human life. The most powerful method for the detection and classification of sleep apnea is the Polysomnogram. However, this method is time-consuming and cost-inefficient. Therefore, several methods focus on using electrocardiogram (ECG) signals to detect sleep apnea. This paper proposed a novel automated approach to detect and classify apneic events from single-lead ECG signals. Wavelet Scattering Transformation (WST) was applied to the ECG signals to decompose the signal into smaller segments. Then, a set of features, including higher-order statistics and entropy-based features, was extracted from the WST coefficients to formulate a search space. The obtained features were fed to a random forest classifier to classify the ECG segments. The experiment was validated using the 10-fold and hold-out cross-validation methods, which resulted in an accuracy of 91.65% and 90.35%, respectively. The findings were compared with different classifiers to show the significance of the proposed approach. The proposed approach achieved better performance measures than most of the existing methodologies.

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

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. 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. 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). 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. 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.

Single-channel EEG processing for sleep apnea detection and differentiation

Single-channel EEG processing for sleep apnea detection and differentiation

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy.

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.