[HTML][HTML] A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory
M Yang, W Liu, H Zhang - Frontiers in Physiology, 2022 - frontiersin.org
Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-
invasive approach for cardiologists to diagnose and classify the nature and severity of …
invasive approach for cardiologists to diagnose and classify the nature and severity of …
[HTML][HTML] Electrocardiogram heartbeat classification for arrhythmias and myocardial infarction
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is
crucial for remote patient monitoring equipment. An accurate ECG signal classification is …
crucial for remote patient monitoring equipment. An accurate ECG signal classification is …
Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss
TF Romdhane, MA Pr - Computers in Biology and Medicine, 2020 - Elsevier
The electrocardiogram (ECG) is an effective tool for cardiovascular disease diagnosis and
arrhythmia detection. Most methods proposed in the literature include the following steps: 1) …
arrhythmia detection. Most methods proposed in the literature include the following steps: 1) …
Deep convolutional neural networks for ECG heartbeat classification using two-stage hierarchical method
Electrocardiogram (ECG) is widely used in computer-aided systems for arrhythmia detection
because it provides essential information for the heart functionalities. The cardiologist uses it …
because it provides essential information for the heart functionalities. The cardiologist uses it …
A robust deep convolutional neural network with batch-weighted loss for heartbeat classification
A Sellami, H Hwang - Expert Systems with Applications, 2019 - Elsevier
The early detection of abnormal heart rhythm has become crucial due to the spike in the rate
of deaths caused by cardiovascular diseases. While many existing works tried to classify …
of deaths caused by cardiovascular diseases. While many existing works tried to classify …
Multi-model deep learning ensemble for ECG heartbeat arrhythmia classification
Managing and treating cardiovascular diseases can be substantially improved by automatic
detection and classification of the heart arrhythmia. In this paper, we introduced a novel …
detection and classification of the heart arrhythmia. In this paper, we introduced a novel …
An explainable attention-based TCN heartbeats classification model for arrhythmia detection
Y Zhao, J Ren, B Zhang, J Wu, Y Lyu - Biomedical Signal Processing and …, 2023 - Elsevier
Abstract Background and Objective: Electrocardiogram (ECG) is a non-invasive tool to
measure the heart's electrical activity. ECG signal based automatic heartbeat classification is …
measure the heart's electrical activity. ECG signal based automatic heartbeat classification is …
An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification
An automatic system for heart arrhythmia classification can perform a substantial role in
managing and treating cardiovascular diseases. In this paper, a deep learning-based multi …
managing and treating cardiovascular diseases. In this paper, a deep learning-based multi …
ECG heartbeat classification based on multi-scale wavelet convolutional neural networks
L El Bouny, M Khalil, A Adib - ICASSP 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
This paper proposes a novel Deep Learning technique for ECG beats classification. Unlike
the traditional Deep Learning models, a new Multi-Scale Wavelet Convolutional Neural …
the traditional Deep Learning models, a new Multi-Scale Wavelet Convolutional Neural …
Automated heartbeat classification exploiting convolutional neural network with channel-wise attention
F Li, J Wu, M Jia, Z Chen, Y Pu - IEEE Access, 2019 - ieeexplore.ieee.org
Long-term Electrocardiogram (ECG) analysis has become a common means of diagnosing
cardiovascular diseases. In order to reduce the workload of cardiologists and accelerate …
cardiovascular diseases. In order to reduce the workload of cardiologists and accelerate …