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
arXiv:2212.13878v17 citationsh-index: 8
Originality Synthesis-oriented
AI Analysis
This addresses the need for diagnostic tools for post-COVID syndrome in patients, but appears incremental as it applies existing methods to new data without specifying performance gains.
The study tackled the problem of diagnosing post-COVID conditions by applying machine learning algorithms to electrocardiogram (ECG) data, identifying a marker in the rhythmogram that can be used for this purpose.
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.