ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects.
Comput Biol Med
; 146: 105540, 2022 07.
Article
in English
| MEDLINE | ID: covidwho-1814280
ABSTRACT
OBJECTIVE:
Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19.METHOD:
We propose a shallow 1-D convolutional neural network (CNN) deep learning architecture, namely ECG-iCOVIDNet, to distinguish ECG data of post-COVID subjects and healthy subjects. Further, we employed ShAP technique to interpret ECG segments that are highlighted by the CNN model for the classification of ECG recordings into healthy and post-COVID subjects.RESULTS:
ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F1-score of 100%.CONCLUSION:
So far, we have not come across any other study with an in-depth ECG signal analysis of the COVID-recovered subjects. In this study, it is shown that the shallow ECG-iCOVIDNet CNN model performed good for distinguishing ECG signals of COVID-recovered subjects from those of healthy subjects. In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model. Successful deployment of such systems can help the doctors identify the changes in the ECG of the post-COVID subjects on time that can save many lives.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Signal Processing, Computer-Assisted
/
COVID-19
Type of study:
Prognostic study
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Comput Biol Med
Year:
2022
Document Type:
Article
Affiliation country:
J.compbiomed.2022.105540
Similar
MEDLINE
...
LILACS
LIS