VALIDITY OF A “NON-CONTACT” DIAGNOSTIC METHOD OF SLEEP ANALYSIS BY THERMOGRAPHIC CAMERA AND ARTIFICIAL INTELLIGENCE

Abstract

<b>Introduction:</b> Thermography has been implemented in different fields of medicine, but its use in sleep medicine has been limited. The objective of this study was to validate the use of a thermographic camera, using an artificial intelligence algorithm, to differentiate wakefulness and sleep in comparison with the conventional polysomnographic (PSG) results. <b>Methods:</b> Staging of wakefulness and sleep was compared between the EEG of PSG recordings and the facial thermographic images obtained by means of a thermographic camera, treated with an artificial intelligence system. The periods of sleep and wakefulness were estimated second by second. Videos of a duration greater than 2.7 hours were considered as valid (61 videos). <b>Results:</b> Characteristics of subjet analized were: age 48±10 years, 73 % men; body mass index 27.0±3.8 kg/m2 and Epworth sleepness scale of 9.0±4.0 points). Of the 943 seconds detected as sleep by thermography, 940 seconds corresponded to sleep phases in PSG (99.7% correct), and of the 867 seconds classified as wakefulness, 766 corresponded to wakefulness in the PSG (88.4% of these). Table I presents the validity results. <b>Conclusions:</b> Treatment of thermographic images, through an artificial intelligence algorithm, is a valid and non-invasive system for determining sleep and wakefulness. Development of these systems could facilitate the implementation of “no contact” diagnosis methods for sleep analysis.