TY - JOUR
T1 - Deep Learning Applications in ECG Analysis and Disease Detection
T2 - An Investigation Study of Recent Advances
AU - Sumalatha, U.
AU - Krishna Prakasha, K.
AU - Prabhu, Srikanth
AU - Nayak, Vinod C.
N1 - Publisher Copyright:
Authors
PY - 2024
Y1 - 2024
N2 - Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG) analysis for early diagnosis and treatment of heart conditions. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), have significantly enhanced the automation, accuracy, and personalization of ECG analysis. This review targets both medical professionals and a broader audience interested in deep learning applications. It explores the evolution of deep learning techniques in ECG analysis, from early CNN applications to current innovations in real-time processing and privacy-preserving methods. The paper discusses various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their impact on diagnostic accuracy for diseases like myocardial infarction. Additionally, it examines ECG-based authentication systems, addressing challenges related to security and privacy, and highlighting recent technological advancements. By providing a detailed overview of these developments, the review offers valuable insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication.
AB - Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG) analysis for early diagnosis and treatment of heart conditions. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), have significantly enhanced the automation, accuracy, and personalization of ECG analysis. This review targets both medical professionals and a broader audience interested in deep learning applications. It explores the evolution of deep learning techniques in ECG analysis, from early CNN applications to current innovations in real-time processing and privacy-preserving methods. The paper discusses various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their impact on diagnostic accuracy for diseases like myocardial infarction. Additionally, it examines ECG-based authentication systems, addressing challenges related to security and privacy, and highlighting recent technological advancements. By providing a detailed overview of these developments, the review offers valuable insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication.
UR - http://www.scopus.com/inward/record.url?scp=85201752700&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85201752700&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3447096
DO - 10.1109/ACCESS.2024.3447096
M3 - Article
AN - SCOPUS:85201752700
SN - 2169-3536
VL - 12
SP - 1
JO - IEEE Access
JF - IEEE Access
ER -