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 …
A cascaded step-temporal attention network for ECG arrhythmia classification
Y Tao, G Yue, K Wang, Y Zhang… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
To improve the accuracy of arrhythmia diagnosis and reduce the recheck time, we design a
cascaded step-temporal attention network called ArrhythmiaNet to classify 15 categories of …
cascaded step-temporal attention network called ArrhythmiaNet to classify 15 categories of …
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 …
HeartNet: Self multihead attention mechanism via convolutional network with adversarial data synthesis for ECG-based arrhythmia classification
Cardiovascular disease is now one of the leading causes of morbidity and mortality.
Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular …
Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular …
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 …
Unsupervised domain adaptation for ECG arrhythmia classification
Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia
diagnosis in clinical practice. In recent years, various algorithms based on deep learning …
diagnosis in clinical practice. In recent years, various algorithms based on deep learning …
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 …
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 …
Automated inter-patient arrhythmia classification with dual attention neural network
Background and objectives Arrhythmia classification based on electrocardiograms (ECG)
can enhance clinical diagnostic efficiency. However, due to the significant differences in the …
can enhance clinical diagnostic efficiency. However, due to the significant differences in the …
MADNN: A multi-scale attention deep neural network for arrhythmia classification
R Duan, X He, Z Ouyang - 2020 Computing in Cardiology, 2020 - ieeexplore.ieee.org
The morphological ECG features for arrhythmia diagnosis are usually identified and
combined on different scales. For example, morphological ECG features can be identified on …
combined on different scales. For example, morphological ECG features can be identified on …