Intelligent ECG-Based COVID-19 Diagnose Device Using CNN Deep Learning Approaches

The COVID-19 virus has caused millions of fatalities worldwide. As a component of our ongoing initiatives to curb the transmission of COVID-19, we are implementing the following measures; a quicker and safer diagnosis is highly desired. The analysis of electrocardiogram (ECG) data has shown usefulness for detecting cardiac disease, stroke, and COVID-19. We developed a fully automated procedure to detect COVID-19 infection in ECGs. Electrocardiographic (ECG) signal-based technologies and computerized models enable the precise identification of COVID-19 following substantial research into the process that the illness is going through, the symptoms that the patient is experiencing, and the laboratory diagnosis. The measures above (accuracy, precision, recall, FSCORE, and false prediction rate) showed excellent results. We discovered that the suggested models for COVID-19 detection with this cutting-edge technology worked well. Logistic regression, InceptionV3, KNN, decision tree, LSTM, SVM, ResNet, Naive Bayes and MobileNet were all equally efficient and precise for binary classification. Our results indicate the idea that it is possible to recognize COVID-19 in ECG images by utilizing deep learning has potential. This is a possibility. Our findings imply that COVID-19 can be recognized by a system that has been trained on digital ECG signals using deep learning.