[HTML][HTML] Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis

Z Zou, B Chen, D **ao, F Tang, X Li - Journal of Medical Internet Research, 2024 - jmir.org
Background Real-time monitoring of pediatric epileptic seizures poses a significant
challenge in clinical practice. In recent years, machine learning (ML) has attracted …

[HTML][HTML] Improved stacked ensemble with genetic algorithm for automatic ecg diagnosis of children living in high-altitude areas

N Zhao, X Li, Y Ma, H Wang, SJ Lee, J Wang - … Signal Processing and …, 2024 - Elsevier
Electrocardiogram (ECG) is a commonly used diagnostic tool in clinical practice that plays a
vital role in the diagnosis and treatment of various heart diseases. Previous studies have …

Dense lead contrast for self-supervised representation learning of multilead electrocardiograms

W Liu, Z Li, H Zhang, S Chang, H Wang, J He… - Information …, 2023 - Elsevier
Usually, manual labeling of large-scale electrocardiograms (ECGs) for deep learning is
always expensive, as it requires considerable effort and time from cardiologists. Currently …

[HTML][HTML] Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification

HL Nguyen, VS Pham, HC Le - Computers, 2024 - mdpi.com
The increasing prevalence of heart diseases has driven the development of automated
arrhythmia classification systems using machine learning and electrocardiograms (ECGs) …

Universal 12-lead ECG representation for signal denoising and cardiovascular disease detection by fusing generative and contrastive learning

J Shi, W Liu, H Zhang, Z Li, S Chang, H Wang… - … Signal Processing and …, 2024 - Elsevier
With the wide use of electrocardiogram (ECG) technology, more and more ECGs have been
collected and stored. However, ECG labeling is costly and laborious, the utilization of …

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification

J Liu, Y Liu, Y **, Z Li, C Qin, X Chen, L Zhao… - Medical Engineering & …, 2024 - Elsevier
As the number of patients with cardiovascular diseases (CVDs) increases annually, a
reliable and automated system for detecting electrocardiogram (ECG) abnormalities is …

A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer

Y Wang, S Hu, J Liu, G Zhong, C Yang - Computers in Biology and …, 2024 - Elsevier
The scarcity of annotated data is a common issue in the realm of heartbeat classification
based on deep learning. Transfer learning (TL) has emerged as an effective strategy for …

Identification and classification of arrhythmic heartbeats from electrocardiogram signals using feature induced optimal extreme gradient boosting algorithm

S Majumder, S Bhattacharya, P Debnath… - Computer Methods in …, 2024 - Taylor & Francis
Arrhythmic heartbeat classification has gained a lot of attention to accelerate the detection of
cardiovascular diseases and mitigating the potential cause of one-third of deaths worldwide …

Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM

X Wan, X Mei, Y Chen, J Luo… - Technology and Health …, 2024 - journals.sagepub.com
Background Deep<? show [AQ ID= GQ5 POS= 12pt]?> neural networks (DNNs) have
recently been significantly applied to automatic arrhythmia classification. However, their …

Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias

N Dominic, B Pardamean - Procedia Computer Science, 2024 - Elsevier
Preventive strategies should be the utmost priority when dealing with diverse patients
suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death …