Deep learning-based ECG arrhythmia classification: A systematic review

Q Xiao, K Lee, SA Mokhtar, I Ismail, ALM Pauzi… - Applied Sciences, 2023 - mdpi.com
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 …

Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations

SW Chen, SL Wang, XZ Qi, SM Samuri… - … Signal Processing and …, 2022 - Elsevier
An electrocardiogram (ECG) is one of the most promising approaches used for the detection
and classification of cardiovascular diseases (CVDs) in recent years. This work reviewed …

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

O Attallah - Computers in biology and medicine, 2022 - Elsevier
The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of
the virus, alleviate lockdown constraints and diminish the burden on health organizations …

CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis

X Yan, D Jiang, L Xiang, Y Xu, Y Wang - Information Fusion, 2024 - Elsevier
When the machinery device operates abnormally, it is not sufficient for fault detection only
via extracting fault features from a single sensor due to the latent fault information may be …

A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge …

M Sepahvand, F Abdali-Mohammadi - Information Sciences, 2022 - Elsevier
Deep learning models developed through multi-lead electrocardiogram (ECG) signals are
considered the leading methods for the automated detection of arrhythmia on computer …

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals

Z Wang, S Stavrakis, B Yao - Computers in Biology and Medicine, 2023 - Elsevier
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

AS Eltrass, MB Tayel, AI Ammar - Neural Computing and Applications, 2022 - Springer
Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several
types of heart disorders. In this study, a novel hybrid approach of deep neural network …

A deep learning approach for atrial fibrillation classification using multi-feature time series data from ecg and ppg

B Aldughayfiq, F Ashfaq, NZ Jhanjhi, M Humayun - Diagnostics, 2023 - mdpi.com
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …

Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records

M Baygin, T Tuncer, S Dogan, RS Tan, UR Acharya - Information Sciences, 2021 - Elsevier
Background and objective Arrhythmia constitute a common clinical problem in cardiology.
The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG …

[HTML][HTML] Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review

MF Safdar, RM Nowak, P Pałka - Computers in Biology and Medicine, 2024 - Elsevier
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the
heart's electrical activity that depicts the movement of cardiac muscles. A review study has …