[HTML][HTML] Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis

LI Xin-Mu, GAO Xin-Yi, G Tse, H Shen-Da… - Journal of geriatric …, 2022 - ncbi.nlm.nih.gov
BACKGROUND The electrocardiogram (ECG) is an inexpensive and easily accessible
investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The …

An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional LSTM network

J Wang - Information Sciences, 2021 - Elsevier
Atrial fibrillation (AF) and atrial flutter (AFL) are the most common arrhythmias. Due to the
similar clinical symptoms, both are one of the main causes of misdiagnosis for physicians …

Risk factor refinement and ensemble deep learning methods on prediction of heart failure using real healthcare records

C Zhou, A Hou, P Dai, A Li, Z Zhang, Y Mu, L Liu - Information Sciences, 2023 - Elsevier
The prediction of heart failure (HF) is crucial in preventing disease progression by
implementing lifestyle changes and pharmacological interventions before the onset of heart …

Heartbeat classification based on single lead-II ECG using deep learning

MF Issa, A Yousry, G Tuboly, Z Juhasz, AH AbuEl-Atta… - Heliyon, 2023 - cell.com
The analysis and processing of electrocardiogram (ECG) signals is a vital step in the
diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for …

Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D) 2PCA and KELM

T Zhang, W Chen, X Chen - Biocybernetics and Biomedical Engineering, 2023 - Elsevier
In order to achieve the accurate identifications of various electroencephalograms (EEGs)
and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST) …

An intelligent computer-aided diagnosis method for paroxysmal atrial fibrillation patients with nondiagnostic ECG signals

M Deng, K Chen, D Huang, D Liang, D Liang… - … Signal Processing and …, 2024 - Elsevier
Classification of ECG signals plays an important role in the field of medical diagnosis.
Despite that much progress has been made in ECG classification in recent years, most of …

Pruned lightweight neural networks for arrhythmia classification with clinical 12-Lead ECGs

Y Liu, J Liu, Y Tian, Y Jin, Z Li, L Zhao, C Liu - Applied Soft Computing, 2024 - Elsevier
Real-time electrocardiogram (ECG) monitoring through portable or wearable devices is
critical for detecting lethal arrhythmias. Despite the accuracy of 12-lead ECGs in clinical …

A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition

J Feng, Y Si, Y Zhang, M Sun… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
In recent years, the incidence of cardiac arrhythmias has been on the rise because of
changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used …

Early prediction of heart disease via LSTM-XGBoost

X Zang, J Du, Y Song - Proceedings of the 2023 9th international …, 2023 - dl.acm.org
With the development of information and technology, especially with the boom in big data,
healthcare support systems are becoming much better. However, an early diagnosis is not …

Co-learning-assisted progressive dense fusion network for cardiovascular disease detection using ECG and PCG signals

H Zhang, P Zhang, F Lin, L Chao, Z Wang, F Ma… - Expert Systems with …, 2024 - Elsevier
Abstract Electrocardiograms (ECGs) and phonocardiograms (PCGs) are two modalities to
provide complementary diagnostic information for improving the early detection accuracy of …