CardioNet: An efficient ECG arrhythmia classification system using transfer learning
A Pal, R Srivastva, YN Singh - Big Data Research, 2021 - Elsevier
The electrocardiogram (ECG) is a noninvasive test used extensively to monitor and
diagnose cardiac arrhythmia. Existing automated arrhythmia classification methods hardly …
diagnose cardiac arrhythmia. Existing automated arrhythmia classification methods hardly …
ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique
Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues
such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine …
such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine …
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 …
ECGformer: Leveraging transformer for ECG heartbeat arrhythmia classification
An arrhythmia, also known as a dysrhythmia, refers to an irregular heartbeat. There are
various types of arrhythmias that can originate from different areas of the heart, resulting in …
various types of arrhythmias that can originate from different areas of the heart, resulting in …
An End‐to‐End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
Electrocardiography (ECG) is a well‐known noninvasive technique in medical science that
provides information about the heart's rhythm and current conditions. Automatic ECG …
provides information about the heart's rhythm and current conditions. Automatic ECG …
A novel method for classification of ECG arrhythmias using deep belief networks
Z Wu, X Ding, G Zhang - International Journal of Computational …, 2016 - World Scientific
In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph
(ECG) arrhythmias classification is proposed. The construction process of ECG classification …
(ECG) arrhythmias classification is proposed. The construction process of ECG classification …
A hybrid deep learning approach for ECG-based arrhythmia classification
Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently
occur in a human's life. These arrhythmias may cause potentially fatal complications, which …
occur in a human's life. These arrhythmias may cause potentially fatal complications, which …
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 …
A transformer-based deep neural network for arrhythmia detection using continuous ECG signals
R Hu, J Chen, L Zhou - Computers in Biology and Medicine, 2022 - Elsevier
Recently, much effort has been put into solving arrhythmia classification problems with
machine learning-based methods. However, inter-heartbeat dependencies have been …
machine learning-based methods. However, inter-heartbeat dependencies have been …
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 …