Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
ECG heartbeat classification using multimodal fusion
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Background and objective Cardiac arrhythmia, which is an abnormal heart rhythm, is a
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
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 …
considered the leading methods for the automated detection of arrhythmia on computer …
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
B Tutuko, S Nurmaini, AE Tondas… - BMC Medical Informatics …, 2021 - Springer
Background Generalization model capacity of deep learning (DL) approach for atrial
fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL …
fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL …
[PDF][PDF] CAB: classifying arrhythmias based on imbalanced sensor data
Intelligently detecting anomalies in health sensor data streams (eg, Electrocardiogram,
ECG) can improve the development of E-health industry. The physiological signals of …
ECG) can improve the development of E-health industry. The physiological signals of …
Deep representation learning with sample generation and augmented attention module for imbalanced ECG classification
Developing an efficient heartbeat monitoring system has become a focal point in numerous
healthcare applications. Specifically, in the last few years, heartbeat classification for …
healthcare applications. Specifically, in the last few years, heartbeat classification for …
Accurate ECG classification based on spiking neural network and attentional mechanism for real-time implementation on personal portable devices
Y Xing, L Zhang, Z Hou, X Li, Y Shi, Y Yuan, F Zhang… - Electronics, 2022 - mdpi.com
Electrocardiogram (ECG) heartbeat classification plays a vital role in early diagnosis and
effective treatment, which provide opportunities for earlier prevention and intervention. In an …
effective treatment, which provide opportunities for earlier prevention and intervention. In an …
ECG-based heartbeat classification using exponential-political optimizer trained deep learning for arrhythmia detection
An electrocardiogram (ECG) computes the electrical functioning of the heart, which is mostly
employed for finding various heart diseases of its feasibility and simplicity. Moreover, some …
employed for finding various heart diseases of its feasibility and simplicity. Moreover, some …
CLECG: A novel contrastive learning framework for electrocardiogram arrhythmia classification
H Chen, G Wang, G Zhang, P Zhang… - IEEE Signal Processing …, 2021 - ieeexplore.ieee.org
Deep learning-based intelligent electrocardiogram (ECG) diagnosis algorithms heavily rely
on large annotated datasets. Unfortunately, in the context of ECG diagnosis, privacy issues …
on large annotated datasets. Unfortunately, in the context of ECG diagnosis, privacy issues …