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 …
HADLN: hybrid attention-based deep learning network for automated arrhythmia classification
M Jiang, J Gu, Y Li, B Wei, J Zhang, Z Wang… - Frontiers in …, 2021 - frontiersin.org
In recent years, with the development of artificial intelligence, deep learning model has
achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In …
achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In …
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 …
Multi-module recurrent convolutional neural network with transformer encoder for ECG arrhythmia classification
The automatic classification of electrocardiogram (ECG) signals has played an important
role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs) …
role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs) …
A LSTM and CNN based assemble neural network framework for arrhythmias classification
This paper puts forward a LSTM and CNN based assemble neural network framework to
distinguish different types of arrhythmias by integrating stacked bidirectional long shot-term …
distinguish different types of arrhythmias by integrating stacked bidirectional long shot-term …
Multi-modality Multi-attention Network for Ventricular Arrhythmia Classification
Z Xu, M Zang, T Liu, S Zhou, C Liu… - Proceedings of the 2023 …, 2023 - dl.acm.org
Classification of Ventricular arrhythmia is a significant topic because Ventricular arrhythmia
is one of the deadliest disease. Electrocardiogram (ECG) plays an important role in …
is one of the deadliest disease. Electrocardiogram (ECG) plays an important role in …
ECG-based expert-knowledge attention network to tachyarrhythmia recognition
Y Tao, Z Li, C Gu, B Jiang, Y Zhang - Biomedical Signal Processing and …, 2022 - Elsevier
An expert-knowledge attention network (EKANet) was designed to improve the accuracy of
arrhythmia diagnosis and reduce the recheck time. This network classifies four …
arrhythmia diagnosis and reduce the recheck time. This network classifies four …
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 …
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 …
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 …