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

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