Evaluation of candidate measures for home-based screening of sleep disordered breathing in Taiwanese bus drivers

Yield of overnight pulse oximetry in screening commercial drivers for obstructive sleep apnea

PURPOSE

To assess the efficacy of overnight pulse oximetry in screening male commercial drivers (CDs) for obstructive sleep apnea (OSA).

METHODS

Consecutive male CDs undergoing their annual scheduled occupational health visit were enrolled from ten transportation facilities. All subjects underwent a home sleep apnea test (HSAT) to determine the Respiratory Event Index (REI). Oxygen desaturation indices (ODIs) below the 3% and 4% thresholds were computed using the built-in HSAT pulse oximeter. We then assessed the association between ODI values and the presence of OSA (defined as an REI ≥ 5 events/hour) as well as moderate to severe OSA (REI ≥ 15 events/hour).

RESULTS

Of 331 CDs recruited, 278 (84%) completed the study protocol and 53 subjects were excluded due to inadequate HSAT quality. The included and excluded subjects were comparable in demographics and clinical characteristics. The included CDs had a median age of 49 years (interquartile range (IQR) = 15 years) and a median body mass index of 27 kg/m2 (IQR = 5 kg/m2). One hundred ninety-nine (72%) CDs had OSA, of which 48 (17%) were with moderate OSA and 45 (16%) with severe OSA. The ODI3 and ODI4 receiving operating characteristic curve value were 0.95 for predicting OSA and 0.98-0.96 for predicting moderate to severe OSA.

CONCLUSION

Overnight oxygen oximetry may be an effective means to screen CDs for OSA.

Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals

INTRODUCTION

Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression.

METHODS

An efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters.

RESULTS

This method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals.

CONCLUSION

According to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

Nocturnal oximetry in the diagnosis of obstructive sleep apnea syndrome in potentially hypoxic patients due to neuromuscular diseases

INTRODUCTION

Polysomnography is the recommended method for the diagnosis of obstructive sleep apnea (OSA); however, it is expensive, uncomfortable, and inaccessible. Alternative diagnostic methods are necessary, and Nocturnal Oximetry (NO) has proven to be reliable. Nevertheless, there have been doubts about its accuracy in patients with a history of hypoxia. Hence, the objective of this study was to evaluate the performance of NO in patients with neuromuscular diseases (NMD).

METHOD

This was a cross-sectional study in patients with NMD suspected of having OSA. We performed a statistical analysis using Spearman's correlation coefficients (SCCs). We used the value of the area under the ROC curve (AUCROC), just as we calculated the sensitivities (Sens) and specificities (Spec) for the chosen variables.

RESULTS

The sample comprised 41 patients; 51.2% with muscular dystrophies and 48.8% with motor neuron diseases, with a predominance of men (63.4%). Median age was 42 (19.7-55) years, body mass index (BMI) was 27.9 (23.8-32) kg/m2, forced vital capacity was 67% (54%-76.5%), and maximum inspiratory pressure was-60 cmH2O (-87.5 to -50). The prevalence of OSA was 75.7%. We analyzed and selected the best four oximetric variables with the following performance in identifying the apnea/hypopnea index >5/h, ODI3/2, cutoff>5/h, AUCROC 0.919, Sens 82.3%, Spec 91.7%; ODI3/5, cutoff>11.2/h, AUCROC 0.904, Sens 82.3%, Spec 87.5%; ODI4/5, cutoff>6.02, AUCROC 0.839, Sens 70.6%, Spec 91.6%, and ODI5/5, cutoff>0.87/h, AUCROC 0.870, Sens 94.1%, and Spec 70.8%.

CONCLUSION

NO can be used as a diagnostic tool for OSA, even in patients with neuromuscular diseases and potentially hypoxic diseases.

Oximetry use in obstructive sleep apnea

INTRODUCTION

Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered: Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection of this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary: Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in populations with significant comorbidities. In the following years, communication technologies and big data analyses will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.

Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Systematic Review, Meta-Analysis, and GRADE Assessment

INTRODUCTION

The purpose of this systematic review is to provide supporting evidence for a clinical practice guideline on the use of actigraphy.

METHODS

The American Academy of Sleep Medicine commissioned a task force of experts in sleep medicine. A systematic review was conducted to identify studies that compared the use of actigraphy, sleep logs, and/or polysomnography. Statistical analyses were performed to determine the clinical significance of using actigraphy as an objective measure of sleep and circadian parameters. Finally, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process was used to assess the evidence for making recommendations.

RESULTS

The literature search resulted in 81 studies that met inclusion criteria; all 81 studies provided data suitable for statistical analyses. These data demonstrate that actigraphy provides consistent objective data that is often unique from patient-reported sleep logs for some sleep parameters in adult and pediatric patients with suspected or diagnosed insomnia, circadian rhythm sleep-wake disorders, sleep-disordered breathing, central disorders of hypersomnolence, and adults with insufficient sleep syndrome. These data also demonstrate that actigraphy is not a reliable measure of periodic limb movements in adult and pediatric patients. The task force provided a detailed summary of the evidence along with the quality of evidence, the balance of benefits and harms, patient values and preferences, and resource use considerations.

Usefulness of overnight pulse oximeter as the sleep assessment tool to assess the 6-year risk of road traffic collision: evidence from the Taiwan Bus Driver Cohort Study

Background

In order to support health service organizations in arranging a system for prevention of road traffic collisions (RTC), it is important to study the usefulness of sleep assessment tools. A cohort study was used to evaluate the effectiveness of subjective and objective sleep assessment tools to assess for the 6-year risk of both first RTC event only and recurrent RTC events.

Methods

The Taiwan Bus Driver Cohort Study (TBDCS) recruited 1650 professional drivers from a large bus company in Taiwan in 2005. The subjects were interviewed in person, completed the sleep assessment questionnaires and had an overnight pulse oximeter survey. Moreover, this cohort of drivers was linked to the National Traffic Accident Database (NTAD) and researchers found 139 new RTC events from 2005 to 2010. Primary outcomes were traffic collisions from NTAD, nocturnal oxygen desaturation index (ODI) from pulse oximeter, Pittsburg sleeping quality score, Epworth daytime sleepiness score, Snore Outcomes Survey score and working patterns from questionnaires. A Cox proportional hazards model and an extended Cox regression model for repeated events were performed to estimate the hazard ratio for RTC.

Results

The RTC drivers had increased ODI4 levels (5.77 ± 4.72 vs 4.99 ± 6.68 events/h; P  = 0.008) and ODI3 levels (8.68 ± 6.79 vs 7.42 ± 7.94 events/h; P  = 0.007) in comparison with non-RTC drivers. These results were consistent regardless of whether ODI was evaluated as a continuous or a categorical variable. ODI4 and ODI3 levels increased the 6-year RTC risks among professional drivers even after adjusting for age, education, history of cardiovascular disease, caffeine intake, sleeping pills used, bus driving experience and shift modes. Moreover, there was an increased trend for ODI between the stratification of the number of RTCs in comparison with the non-RTC group. In the extended Cox regression models for repeated RTC events with the Anderson and Gill intensity model and Prentice-Williams-Petersen model, measurement of ODI increased hazards of the subsequent RTC events.

Conclusion

This study showed that an increase in the 6-year risk of RTC was associated with objective measurement of ODI for a sign of sleep-disordered breathing (SDB), but was not associated with self-reported sleeping quality or daytime sleepiness. Therefore, the overnight pulse oximeter is an effective sleep assessment tool for assessing the risk of RTC. Further research should be conducted regarding measures to prevent against SDB among professional drivers.

Utility of overnight pulse oximeter as a screening tool for sleep apnea to assess the 8-year risk of cardiovascular disease: Data from a large-scale bus driver cohort study

BACKGROUND

Professional drivers' work under conditions predisposes them for development of sleep-disordered breathing (SDB) and cardiovascular disease (CVD). However, the effect of SDB on CVD risk among professional drivers has never been investigated. A cohort study was used to evaluate the effectiveness of overnight pulse oximeter as a sleep apnea screening tool to assess the 8-year risk of CVD events.

METHODS

The Taiwan Bus Driver Cohort Study (TBDCS) recruited 1014 professional drivers in Taiwan since 2005. The subjects completed questionnaire interview and overnight pulse oximeter survey. This cohort was linked to the National Health Insurance Research Dataset (NHIRD). Researchers found 192 CVD cases from 2005 to 2012. Cox proportional hazards model was performed to estimate the hazard ratio for CVD. The statistical analysis was performed using SAS software in 2015.

RESULTS

ODI4 and ODI3 levels increased the 8-year CVD risk, even adjusting for CVD risk factors (HR: 1.36, 95% CI: 1.05 to 1.78; p=0.022, and HR: 1.40, 95% CI: 1.03 to 1.90; p=0.033). ODI4 and ODI3 thresholds of 6.5 and 10events/h revealed differences of CVD risks (HR: 1.72, 95% CI: 1.00 to 2.95; p=0.048, and HR: 1.76, 95% CI: 1.03 to 3.03; p=0.041). Moreover, the ODI levels had an increased risk for hypertensive disease (not including essential hypertension).

CONCLUSIONS

This study concludes that ODI for a sign of SDB is an independent predictor of elevated risk of CVD. Further research should be conducted regarding measures to prevent against SDB in order to reduce CVD risk in professional drivers.