ABSTRACT
An electrocardiogram, often known as an ECG, is a diagnostic tool that measures the electrical activity of the heart in order to identify potential heart abnormalities. Although the normal 12-lead ECG is the dominant approach in cardiac diagnostics, it is still challenging to identify distinct heart illnesses using a single lead or a reduced number of leads. Automatic diagnosis of cardiac abnormalities via the ECG with a reduced lead system (less than the typical 12-lead system) may give a helpful diagnostic alternative to traditional 12-lead ECG equipment that is both simple to use and less expensive. This alternative uses fewer leads than the standard system. This study considers the use of Recurrent Neural Networks Long Short-Term Memory (RNN- LSTM) to identify the ability to use less standard ECG leads to detect cardiac abnormalities using various lead combinations, including 6, 4, 3, 2, 1, and 12 lead ECG data. The results of this investigation are presented in this article. Data pre-processing, model design, and hyperparameter tuning are all essential for RNN-LSTM multi-label classification. The initial step was to pre-process the ECG readings to eliminate the base-line wander noise for ECG signals;the next stage is lead combination selection and clipped to have an equal duration of 10 seconds at various used leads. The gathered results show a possibility of using a single lead instead of multiple leads for preliminary cardiovascular diseases (CVDs) identification. It is a critical issue, especially during emergencies such as the COVID- 19 pandemic or in crowded hospitals when medical resources are limited and online (internet-based) monitoring technologies are vital. © 2022 IEEE.
ABSTRACT
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.