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 …
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 …
Generalization of convolutional neural networks for ECG classification using generative adversarial networks
Electrocardiograms (ECGs) play a vital role in the clinical diagnosis of heart diseases. An
ECG record of the heart signal over time can be used to discover numerous arrhythmias. Our …
ECG record of the heart signal over time can be used to discover numerous arrhythmias. Our …
Generative adversarial network with transformer generator for boosting ECG classification
Y Xia, Y Xu, P Chen, J Zhang, Y Zhang - Biomedical Signal Processing and …, 2023 - Elsevier
Arrhythmia is an important group of cardiovascular diseases, which can suddenly attack and
cause sudden death, or continue to affect the heart and cause its failure. Electrocardiogram …
cause sudden death, or continue to affect the heart and cause its failure. Electrocardiogram …
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 …
Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …
critical to timely medical treatment to save patients' lives. Routine use of the …
Feature matching based ECG generative network for arrhythmia event augmentation
F Cao, A Budhota, H Chen… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
Recent developments in the field of deep learning has shown a rise in its use for clinical
applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia …
applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia …
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 …
Constrained transformer network for ECG signal processing and arrhythmia classification
Background Heart disease diagnosis is a challenging task and it is important to explore
useful information from the massive amount of electrocardiogram (ECG) records of patients …
useful information from the massive amount of electrocardiogram (ECG) records of patients …
A new transfer learning approach to detect cardiac arrhythmia from ECG signals
Deep Learning (DL) has turned into a subject of study in different applications, including
medical field. Finding the irregularities in Electrocardiogram (ECG) is a critical part in …
medical field. Finding the irregularities in Electrocardiogram (ECG) is a critical part in …