A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin. © 2023 IEEE.

Development of Real-Time Methods for Calculating Cardiointervalograms for Estimating the State of the Cardiovascular System Using a Single Photoplethysmogram Signal

We have developed four real-time methods for calculating a cardiointervalogram from a photoplethysmogram signal to calculate the total percentage of phase synchronization for estimating the state of the cardiovascular system. The methods were compared with the classical method of calculating a cardiointervalogram from an electrocardiogram signal using signals recorded from healthy volunteers and patients with COVID-19. © 2022 IEEE.

Arrhythmia Detection on ECG Signal Using Neural Network Approach

The world has been shook by a rigorous pandemic covid-19 additionally it has accentuated a consequentiality on automating the health sectors from manually reading the reports to utilizing machine learning as an implement to getting the results of findings of sundry reports in an automated manner. There are many studies which have proved that the persons suffering from corona virus had optically discerned its effect on heart health. In rigorous cases it lead to cardiac apprehend proving it to be fatal for the patients. ECG (Electro cardiogram) is undertaken on patients to monitor their heart health;the ECG reports are then manually checked by medicos to conclude about heart health of a person. Cardiology is a study of heart and includes a variety of intricate diseases to be studied. This paper presents an efficient way of arrhythmia detection utilizing dataset which would be subsidiary for implementation of machine learning in this disease detection. Neural network has been utilized in the proposed work and is found to be 99% efficient thereby exhibiting a precise and tested method to further facilitate automation in this sector. © 2021 IEEE.

Building an AI Model on ECG Data for Identifying Burnout/Stressed Healthcare Workers Involved in Covid-19 Management

An electrocardiogram (ECG) is used to monitor electrical activity of the heart. ECG data with 12 leads can help in detecting various cardiac (heart) problems. One of the significant factors that contribute to various cardiac diseases is work/personal stress. Use of various machine and deep learning approaches to analyse ECG data has yielded promising results in the field of predictive and diagnostic healthcare with less human error or bias. In our study, 10sec of 500Hz, 12-lead ECG samples were collected from the healthcare workers, who were involved directly or indirectly in taking care of COVID-19 patients. The present study was designed to determine whether Healthcare workers were stressed by using only ECG as input to a deep learning model. To the best of our knowledge, no earlier ECG based study has been carried out to identify stressed persons among the healthcare workers who are giving support to COVID-19 patients. In this study, ECG data of healthcare workers giving services to COVID-19 patients is utilized. This data was collected from four tertiary academic care centres of India. A modified version of AlexNet is utilized on this data that is able to identify a stressed healthcare worker with 99.397% accuracy and 99.411% AUC score. Successful deployment of such systems can help governments and hospital administrations make appropriate policy decisions during pandemics. © 2021 IEEE.