A review on the impact of deep learning in the identification of atrial septal defect and a comparative study on the algorithms employed in the imaging modalities

Abstract

Among the congenital coronary heart diseases, atrial septal defect constitutes the 1/3 most common type. In many cases, the affected person stays asymptomatic for the duration of the youth even having big shunts. Methodologies that may be hired for figuring out the defects are : echocardiogram , chest X-ray, electrocardiogram, cardiac catheterization, MRI, CT scan, phonocardiogram . Deep learning may be correctly utilised for the automatic estimation of the illness from the test result. The purpose of this review paper is to offer an perception into ASD, the strategies for figuring out it and the application of deep learning models for distinguishing the illness. The paper opinions diverse algorithms used for identity of defects also points out the restrictions of every algorithm.

Integrating deep learning architectures for improved arrhythmia detection in electrocardiogram signals

“…Convolutional layers are designed to learn features from input data, such as edges, lines, and shapes, by dragging filters over the input and performing multiplication and sum operations on each. Element [37]. Pooling layers are used to reduce the size of the output of the convolutional layers, which reduces the computation required by the network.…”