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 identificationandmonitoring
Recommended Citation
Jameel, Ghasaq Saad; Al-Turfi, M N.; Mahdi, Dr. Hussain Falih; and Hussain, Saba Nasser
(2024)
"Designing and Implementing an ECG Reader and Heart Disease Classification Device Using Deep Learning (Comprehensive Review),"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
3, Article 41.
DOI: https://doi.org/10.52866/ijcsm.2024.05.03.034
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss3/41
