Using machine learning algorithms to determine the post-COVID state of a person by his rhythmogram
Sergey Stasenko, Andrey Kovalchuk, Eremin Evgeny, Natalya Zarechnova,, Maria Tsirkova, Sergey Permyakov, Sergey Parin, Sofia Polevaya
TL;DR
This paper demonstrates that machine learning algorithms can identify a post-COVID state in individuals by analyzing ECG rhythmogram data, revealing a specific marker for diagnosis.
Contribution
The study introduces a novel ECG marker for post-COVID state detection and applies machine learning to diagnose it effectively.
Findings
Identified a specific ECG marker associated with post-COVID state
Machine learning algorithms successfully classified post-COVID status from ECG data
Proved the diagnostic potential of ECG rhythmogram analysis for post-COVID conditions
Abstract
In this study we applyed machine-learning algorithms to determine the post-COVID state of a person. During the study, a marker of the post-COVID state of a person was found in the electrocardiogram data. We have shown that this marker in the patient's ECG signal can be used to diagnose a post-COVID state.
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Taxonomy
TopicsHeart Rate Variability and Autonomic Control · Non-Invasive Vital Sign Monitoring · ECG Monitoring and Analysis