Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Abstract

Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

“…This obviates the need for manual feature extraction, enabling the model to autonomously discern intricate patterns. Notably, Convolutional Neural Networks (CNNs), Deep Neural Network (DNN), Transfer Learning Models (TLM), Deep Belief Network (DBM), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Bi-directional-LSTM (Bi-LSTM) and others related model within the deep learning paradigm excel in learning spatial and temporal dependencies from raw EEG signals, making them adept at tasks like image classification and sequential data analysis, respectively ( Tan et al, 2017 ; Merlin Praveena et al, 2022 ; Rahul and Sharma, 2022 ; Chaitanya et al, 2023 ). However, deep learning models often necessitate substantial datasets for effective generalization, and data augmentation techniques may be required to address data scarcity.…”

“…This A module of LSTM network. (Tan et al, 2017;Merlin Praveena et al, 2022;Rahul and Sharma, 2022;Chaitanya et al, 2023). However, deep learning models often necessitate substantial datasets for effective generalization, and data augmentation techniques may be required to address data scarcity.…”