Multi-scale and attention based ResNet for heartbeat classification
H Zhang, G Yang, Y Huang, F Yuan… - 2020 25th International …, 2021 - ieeexplore.ieee.org
This paper presents a novel deep learning framework for the electrocardiogram (ECG)
heartbeat classification. Although there have been some studies with excellent overall …
heartbeat classification. Although there have been some studies with excellent overall …
ECG arrhythmia heartbeat classification using deep learning networks
Y Yang, L Jin, Z Pan - … , CloudComp 2020, Qufu, China, December 11-12 …, 2021 - Springer
The electrocardiogram (ECG) records the process of depolarization and repolarization of the
heart and contains many important details related to the condition of the human heart. In this …
heart and contains many important details related to the condition of the human heart. In this …
Fusing transformer model with temporal features for ECG heartbeat classification
ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. Traditional
heartbeat classification methods rely on handcrafted features and often fail to learn …
heartbeat classification methods rely on handcrafted features and often fail to learn …
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 …
MTDL-NET: Morphological and Temporal Discriminative Learning for Heartbeat Classification
Heartbeat classification based on Electrocardiogram (ECG) signal is crucial to the clinical
diagnosis of heart diseases, which has attracted special interest both industrially and …
diagnosis of heart diseases, which has attracted special interest both industrially and …
Heartbeat classification method combining multi-branch convolutional neural networks and transformer
F Zhou, J Wang - Iscience, 2024 - cell.com
The detection and classification of arrhythmias are crucial steps in diagnosing
cardiovascular diseases. However, current deep learning-based classification methods …
cardiovascular diseases. However, current deep learning-based classification methods …
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) …
Automated Heartbeat Classification for Arrhythmia Patients Using a Deep Convolutional Neural Network
The electrocardiogram (ECG) is widely used for diagnosing heart diseases, including
arrhythmia, due to its noninvasive nature and simplicity. Accurate detection and …
arrhythmia, due to its noninvasive nature and simplicity. Accurate detection and …
Adversarial multi-task learning for robust end-to-end ECG-based heartbeat classification
In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-
consuming process. Furthermore, this process is error-prone due to noise from the recording …
consuming process. Furthermore, this process is error-prone due to noise from the recording …
Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and
diagnose several abnormal arrhythmias. While there have been remarkable improvements …
diagnose several abnormal arrhythmias. While there have been remarkable improvements …