Deep learning-based ECG arrhythmia classification: A systematic review
Deep learning (DL) has been introduced in automatic heart-abnormality classification using
ECG signals, while its application in practical medical procedures is limited. A systematic …
ECG signals, while its application in practical medical procedures is limited. A systematic …
Enhancing dynamic ECG heartbeat classification with lightweight transformer model
Arrhythmia is a common class of Cardiovascular disease which is the cause for over 31% of
all death over the world, according to WHOs' report. Automatic detection and classification of …
all death over the world, according to WHOs' report. Automatic detection and classification of …
Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records
Background and objective Arrhythmia constitute a common clinical problem in cardiology.
The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG …
The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG …
An arrhythmia classification model based on vision transformer with deformable attention
Y Dong, M Zhang, L Qiu, L Wang, Y Yu - Micromachines, 2023 - mdpi.com
The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart
activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia …
activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia …
Time series based data explorer and stream analysis for anomaly prediction
All over the world, time series‐based anomaly prediction plays a vital role in all walks of life
such as medical monitoring in hospitals and climate and environment risks. In the present …
such as medical monitoring in hospitals and climate and environment risks. In the present …
Multimodal fusion-based image hiding algorithm for secure healthcare system
The development of artificial intelligence plays a significant role of multimedia applications,
especially in the healthcare domain. However, it has brought about the problem of sensitive …
especially in the healthcare domain. However, it has brought about the problem of sensitive …
Optical electrocardiogram based heart disease prediction using hybrid deep learning
AL Golande, T Pavankumar - Journal of Big Data, 2023 - Springer
The diagnosis and categorization of cardiac disease using the low-cost tool
electrocardiogram (ECG) becomes an intriguing study topic when contemplating intelligent …
electrocardiogram (ECG) becomes an intriguing study topic when contemplating intelligent …
Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism
H Li, J Han, H Zhang, X Zhang, Y Si, Y Zhang… - Computers in Biology …, 2024 - Elsevier
Background: Automatic abnormalities detection based on Electrocardiogram (ECG)
contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of …
contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of …
Arrhythmia detection using TQWT, CEEMD and deep CNN-LSTM neural networks with ECG signals
Cardiac arrhythmia is a typically clinical manifestation of cardiovascular disease which leads
to serious health problem. Detection of arrhythmia is traditionally relying on manual …
to serious health problem. Detection of arrhythmia is traditionally relying on manual …
A causal intervention scheme for semantic segmentation of quasi-periodic cardiovascular signals
Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and
capture anomaly with cardiovascular signals. However, in the field of deep semantic …
capture anomaly with cardiovascular signals. However, in the field of deep semantic …