Designing and Implementing an ECG Reader and Heart Disease Classification Device Using Deep Learning (Comprehensive Review)

Abstract

Cardiovascular cases remain a significant health care concern, necessitating new development
methods for their prompt and accurate identification where a promising advancements is demonstrated using Deep
Learning (DL) in automating heart anomalies detection, including irregular heartbeats, heart attacks, and abnormal
heart rhythms, through the analysis of electrocardiogram (ECG) signals, which guided this article to explore the
integration of DL methodologies in examining ECG data to enhance the diagnosis of various cardiac ailments and
delves into the development of novel ECG devices designed to facilitate efficient data acquisition while ensuring
patient comfort and accessibility.
The devices used must provide real-time monitoring, seamless integration with DL models, enabling continuous
and personalized heart disease detection for each patient, hence this paper offers an overview of the synergy
between innovative ECG device technology and advanced DL algorithms heralds a transformative era in heart
disease diagnosis, emphasizing patient-centered care, the current research landscape and the challenges ahead, and
the promising possibilities on the horizon, highlighting the potential to revolutionize cardiovascular healthcare
through early, precise, and personalized heart diseases identification and monitoring