Sleep disorders are growing more and more prevalent in lives of modern city residents. Thats’s why the analysis of sleep stages by means of a polysomnographic study is getting more and more relevant not only for science, but for everyday clinical practice. Scientists from N.G. Chernyshevsky Saratov State University and Moscow’s National Medical Research Center for Preventive Medicine have proposed an automated algorithm for detecting different stages of sleep based on frequency-time analysis of biophysical signals recorded during night monitoring with the use of a parallel computing technology based on Graphics Processing Units (GPUs). The work has been presented at the XXIV Congress of the Physiological Society named after I. P. Pavlov in Saint Petersburg.
According to the authors of the work, despite significant advances in the field of automatic algorithm development for detecting different sleep stages for medical purposes, these algorithms are rarely put into practice. The problem lies in its low level of accuracy, which is related to high variability of polysomnographic recordings and time-consuming computer analysis. The new approach takes advantage of parallel computing with GPUs and is based on using of uninterrupted wavelet transform methods. As reported by the authors, the new method has highlighted its good functionality and relatively high quality: the data has coincided with the somnologist’s marking by 81%, which is in no way inferior to earlier algorithms, but significantly reduces time spent on analysis by using GPU. The authors think that training the algorithm during the EEG of a specific patient before sleep will allow to increase the study’s accuracy even more.
The material has been prepared with the financial support of Ministry of Education of Russia within the federal project «Popularization of science and technology».
