Year: 2025
Type: article
Abstract: Heart abnormalities remain a leading cause of mortality worldwide, necessitating the development of precise diagnostic tools. This study explores the application of deep learning, a transformative approach in artificial intelligence, to enhance the detection of cardiac irregularities. Utilizing convolutional and recurrent neural networks, the proposed framework analyzes electrocardiogram (ECG) signals and medical imaging data to accurately identify arrhythmias, structural heart diseases, and other anomalies. Comprehensive experimentation on benchmark datasets demonstrates the model's robustness, scalability, and potential for integration into clinical practice. The findings underscore deep learning's promise as a non-invasive, efficient, and reliable solution for improving heart disease diagnosis and patient care
Source: Frontiers in Health Informatics
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7218302072