Lobachevsky University scientists propose a new way to diagnose post-COVID syndrome
UNN researchers have proposed a new way to diagnose post-COVID syndrome. It is known that most deaths from the effects of coronavirus are caused by cardiovascular disorders. The neural network developed by Nizhny Novgorod scientists detects post-COVID syndrome by abnormalities in heart rhythm variability, helping to identify the negative impact of COVID-19 on the heart and blood vessels.
The study was carried out by scientists from the Department of Psychophysiology (Faculty of Social Sciences) and the Department of Neurotechnology (Institute of Biology and Biomedicine) of Lobachevsky University together with their colleagues from the COVID Hospital of the Volga District Medical Center of the Federal Medical and Biological Agency and from the Institute of Applied Physics of the Russian Academy of Sciences.
"Electrocardiograms of "red zone" patients revealed abnormal low-amplitude areas - cardiac spikes, which occur in almost 90% of cases. In the pre-COVID ECGs from our archives, such phenomena are observed only in 3% of cases, which allows us to consider cardiac spikes as COVID-specific markers of the functioning of regulatory systems," said Sofia Polevaya, Head of the UNN Department of Psychophysiology.
Cardiorhythmograms were measured using a miniature wireless ECG sensor and a mobile app. Based on a database of rhythmograms from patients with COVID-19, a neural network has been developed and trained that is able to distinguish post-COVID syndrome from other functional disorders in the heart and vessels using ECG data.
Nizhny Novgorod scientists’ invention makes it possible not only to register post-COVID syndrome in humans but also to monitor the functional state of patients affected by any strain of the novel coronavirus infection.
The invention was patented in September 2022.
Research was performed at the Laboratory of Advanced Methods for Analysis of Multidimensional Data of Lobachevsky University’s Institute of Information Technology, Mathematics and Mechanics under the megagrant "Scalable Networks of Artificial Intelligence Systems for Analysis of High-Dimensional Data" (Project № 075-15-2021-634).
