Abstract
Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.
Highlights
The quality of sleep examination is getting more relevance in the current healthcare systems since sleep related complaints are the second most common reason for pursuing medical care that is only superseded by the feel of pain [1]
Each optimal feature set was produced by sequential forward selection (SFS), an iterative process that considered the was produced by sequential forward selection (SFS), an iterative process that considered the minimum minimum average misclassification error as the decision metric for the feature average misclassification error as the decision metric for the feature selection [19]
It was verified that the performance of the obstructive sleep apnea (OSA) assessment algorithm same range as the available in the state of the thedespite less complexity evaluation) isisininthe the same range as works the works available in the stateartofdespite the art the less of the proposed was alsoItverified that the m-apnea-hypopnea index (AHI)-tib has a strong with the complexity of themethod
Summary
The quality of sleep examination is getting more relevance in the current healthcare systems since sleep related complaints are the second most common reason for pursuing medical care that is only superseded by the feel of pain [1]. Poor sleep quality is directly associated with the occurrence of a sleep related disorder (more than 60 disorders have been identified by the International Classification of Sleep Disorders) [3]. Among these disorders, the sleep related breathing disorders are the most prevalent and obstructive sleep apnea (OSA) is the most common in the adult population. The severity of the disorder is commonly assessed by the apnea-hypopnea index (AHI) that is given by the ratio of the number of apnea and hypopnea events per hour of sleep [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
