# Using machine learning algorithms to determine the post-COVID state of a person by his rhythmogram

arXiv: https://arxiv.org/abs/2212.13878 · HTML: https://mlsanity.com/paper/2212.13878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 85 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks](https://mlsanity.com/paper/2208.05433/) (2022)
- [Failure of neuron network coherence induced by SARS-CoV-2-infected astrocytes](https://mlsanity.com/paper/2210.01014/) (2022)

## Abstract (arXiv)

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.