Automatic Video Analysis for Obstructive Sleep Apnea Diagnosis

Machine learning methods for adult OSAHS risk prediction

BACKGROUND

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS.

MATERIALS AND METHODS

Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938.

CONCLUSION

We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.

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%.

Factors affecting polysomnography compliance and delays to surgical treatment of obstructive sleep apnea

OBJECTIVE

To evaluate adherence to polysomnography in pediatric patients and determine if demographics, socioeconomic status, polysomnography indication, or prior otolaryngology intervention are associated with polysomnography adherence and time to definitive surgery.

STUDY DESIGN

Retrospective review study.

SETTING

Tertiary-care children's hospital.

METHODS

Electronic medical record was queried to identify patients ordered for a sleep study between January and May 2019. Demographic information, time to sleep study, and time to surgery were collected and calculated.

RESULTS

304 patients were recommended to obtain polysomnography, with adherence rate of 65.4%. There was no significant difference in adherence or loss to follow-up rates based on patient sex, age, language, socioeconomic status, state of residence, single-parent status, or polysomnography indication. There was no difference between time to surgery for patients who did or did not obtain polysomnography (181 vs. 161 days, P = .51). Patients with prior otolaryngology intervention were more likely to obtain polysomnography and less likely to be lost to follow-up (P < .05). Median household income demonstrated a significant inverse relationship with time to polysomnography (P < .05) as well as time to surgery (P < .05). Medically complex patients tended to experience longer time to surgery compared with non-complex patients.

CONCLUSION

Families with lower socioeconomic status or medically complex children may require assistance to obtain polysomnography and pursue surgery for sleep-disordered breathing. Patients without prior otolaryngology intervention may be less likely to follow up and may need assistance with navigating the polysomnography process.

Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs' performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments.

Validity of non-contact methods for diagnosis of Obstructive Sleep Apnea: a systematic review and meta-analysis

STUDY OBJECTIVE

Obstructive Sleep Apnea (OSA) is associated with increased perioperative cardiac, respiratory and neurological complications. Pre-operative OSA risk assessment is currently done through screening questionnaires with high sensitivity but poor specificity. The objective of this study was to evaluate the validity and diagnostic accuracy of portable, non-contact devices in the diagnosis of OSA as compared with polysomnography.

DESIGN

This study is a systematic review of English observational cohort studies with meta-analysis and risk of bias assessment.

SETTING

Pre-operative, including in the hospital and clinic setting.

PATIENTS

Adult patients undergoing sleep apnea assessment using polysomnography and an experimental non-contact tool.

INTERVENTIONS

A novel non-contact device, which does not utilize any monitor that makes direct contact with the patient's body, in conjunction with polysomnography.

MEASUREMENTS

Primary outcomes included pooled sensitivity and specificity of the experimental device in the diagnosis of obstructive sleep apnea, in comparison to gold-standard polysomnography.

RESULTS

Twenty-eight of 4929 screened studies were included in the meta-analysis. A total of 2653 patients were included with the majority being patients referred to a sleep clinic (88.8%). Average age was 49.7(SD±6.1) years, female sex (31%), average body mass index of 29.5(SD±3.2) kg/m², average apnea-hypopnea index (AHI) of 24.7(SD±5.6) events/h, and pooled OSA prevalence of 72%. Non-contact technology used was mainly video, sound, or bio-motion analysis. Pooled sensitivity and specificity of non-contact methods in moderate to severe OSA diagnosis (AHI > 15) was 0.871 (95% CI 0.841,0.896, I² 0%) and 0.8 (95% CI 0.719,0.862), respectively (AUC 0.902). Risk of bias assessment showed an overall low risk of bias across all domains except for applicability concerns (none were conducted in the perioperative setting).

CONCLUSION

Available data indicate contactless methods have high pooled sensitivity and specificity for OSA diagnosis with moderate to high level of evidence. Future research is needed to evaluate these tools in the perioperative setting.

Dream enactment behavior: review for the clinician

NONE

Dream enactment behavior commonly occurs on occasion in normal children and adults. Disruptive and frequent dream enactment behavior may come to the attention of the clinician either as the primary reason for consultation or as a prominent characteristic of a patient with other sleep disorders. Questioning patients with chronic neurologic and psychiatric disorders may also reveal previously unrecognized behavior. In the absence of sleep pathology, process of dream enactment likely begins with active, often emotionally charged dream content that may occasionally break through the normal REM sleep motor suppressive activity. Disrupted sleep resulting from many possible causes, such as circadian disruption, sleep apnea, or medications, may also disrupt at least temporarily the motor-suppressive activity in REM sleep, allowing dream enactment to occur. Finally, pathological neurological damage in the context of degenerative, autoimmune, and infectious neurological disorders may lead to chronic recurrent and severe dream enactment behavior. Evaluating the context, frequency, and severity of dream enactment behavior is guided first and foremost by a structured approach to the sleep history. Physical exam and selected testing support the clinical diagnosis. Understanding the context and the likely cause is essential to effective therapy.

Non-contact Apnea-Hypopnea Index Estimation using Near Infrared Video

Sleep apnea is a highly prevalent and underdiagnosed sleep disorder characterized by repeated intermittent interruptions to breathing. Sleep apnea severity is measured with the apnea-hypopnea index (AHI), defined as the number of apnea or hypopnea events per hour of sleep. We hypothesize that respiratory related motion features extracted from infrared video can be used to reliably estimate AHI. The 3 feature variables chosen for apneic event estimation, and separately for sleep versus awake estimation, were: the estimated respiratory rate, the magnitude of respiratory movement, and the amount of movements. Leave-one-person-out cross validation on data from 19 participants was used to train and test a random forest binary classifier to detect apneas and hypopneas. Linear regression of the number of estimated events over estimated sleep duration and the total duration of estimated apneic events over estimated sleep duration was used to estimate AHI. Sleeping versus awake segments was estimated with mean ± standard deviation accuracy of 76.0% ± 17.7%. AHI was estimated with correlation coefficient of 0.76 (p <; 0.01) to the clinical gold standard AHI. Accuracy of 78.9% was achieved for classifying AHI ≥ 15, with sensitivity of 70.0%, specificity of 88.9%, and precision of 87.5%. Motion features extracted from infrared video are concluded to be suitable for estimation of AHI.

Digital Health and Sleep-Disordered Breathing: A Systematic Review and Meta-Analysis

STUDY OBJECTIVES

Sleep disorders in most individuals remain undiagnosed and without treatment. The use of novel tools and mobile technology has the potential to increase access to diagnosis. The objective of this study was to perform a quantitative and qualitative analysis of the available literature evaluating the accuracy of smartphones and portable devices to screen for sleep-disordered breathing (SDB).

METHODS

A literature review was performed between February 18, 2017 and March 15, 2017. We included studies evaluating adults with SDB symptoms through the use mobile phones and/or portable devices, using standard polysomnography as a comparison. A qualitative evaluation of studies was performed with the QUADAS-2 rating. A bivariate random-effects meta-analysis was used to obtain the estimated sensitivity and specificity of screening SDB for four groups of devices: bed/mattress-based, contactless, contact with three or more sensors, and contact with fewer than three sensors. For each group, we also reported positive predictive values and negative predictive values for mild, moderate, and severe obstructive sleep apnea (OSA) screening.

RESULTS

Of the 22 included studies, 18 were pooled in the meta-analysis. Devices that were bed/mattress-based were found to have the best sensitivity overall (0.921, 95% confidence interval [CI] 0.870, 0.953). The sensitivity of contactless devices to detect mild OSA cases was the highest of all groups (0.976, 95% CI 0.899, 0.995), but provided a high false positive rate (0.487, 95% CI 0.137, 0.851). The remaining groups of devices showed low sensitivity and heterogeneous results.

CONCLUSIONS

This study evidenced the limitations and potential use of portable devices in screening patients for SDB. Additional research should evaluate the accuracy of devices when used at home.