Interpretable AI Model-Based Predictions of ECG changes in COVID-recovered patients

ABSTRACT

COVID-19 has caused immense social and economic losses throughout the world. Subjects recovered from COVID are learned to have complications. Some studies have shown a change in the heart rate variability (HRV) in COVID-recovered subjects compared to the healthy ones. This change indicates an increased risk of heart problems among the survivors of moderate-to-severe COVID. Hence, this study is aimed at finding HRV features that get altered in COVID-recovered subjects compared to healthy subjects. Data of COVID-recovered and healthy subjects were collected from two hospitals in Delhi, India. Seven ML models have been built to classify healthy versus COVID-recovered subjects. The best-performing model was further analyzed to explore the ranking of altered heart features in COVID-recovered subjects via AI interpretability. Ranking of these features can indicate cardiovascular health status to doctors, who can provide support to the COVID-recovered subjects for timely safeguard from heart disorders. To the best of our knowledge, this is the first study with an in-depth analysis of the heart status of COVID-recovered subjects via ECG analysis. © 2021 IEEE.