Does automated feature selection improve the diagnosis of obstructive sleep apnea compared to the STOP-BANG questionnaire in patients undergoing polysomnography?
Automated feature selection from polysomnography data can accurately detect moderate to severe OSA, potentially enabling preliminary diagnosis via smartwatch devices.
The paper presents a methodology of computer data analysis supporting medical diagnosis of obstructive sleep apnea (OSA) based on the results of polysomnography. Based on a database of 5114 patients, methods of detecting OSA with high accuracy have been developed. It has been also confirmed that obesity is an important risk factor. The methods of computer diagnostics have been compared with commonly used STOP-BANG questionnaire. The key stage of methodology referred to distinguished features that are most related to moderate or severe OSA presence, and are easy to gather at the same time. As a result of our studies we can conclude that it is possible to use smartwatch devices in order to develop a system of preliminary diagnostics of obstructive sleep apnea, which allows in the future for increased availability of apnea tests, reduced costs and earlier diagnosis.
Wosiak et al. (Wed,) studied this question.