ECG arrhythmia classification using a 2-D convolutional neural network
In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification
method using a deep two-dimensional convolutional neural network (CNN) which recently …
method using a deep two-dimensional convolutional neural network (CNN) which recently …
Arrhythmic heartbeat classification using 2d convolutional neural networks
Background Electrocardiogram (ECG) is a method of recording the electrical activity of the
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …
heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any …
12-Lead ECG arrhythmia classification using cascaded convolutional neural network and expert feature
X Yang, X Zhang, M Yang, L Zhang - Journal of Electrocardiology, 2021 - Elsevier
Owing to widely available digital ECG data and recent advances in deep learning
techniques, automatic ECG arrhythmia classification based on deep neural network has …
techniques, automatic ECG arrhythmia classification based on deep neural network has …
A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification
Effective detection of arrhythmia is an important task in the remote monitoring of
electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the …
electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the …
ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features
Due to the recent advances in the area of deep learning, it has been demonstrated that a
deep neural network, trained on a huge amount of data, can recognize cardiac arrhythmias …
deep neural network, trained on a huge amount of data, can recognize cardiac arrhythmias …
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 …
A study on arrhythmia via ECG signal classification using the convolutional neural network
M Wu, Y Lu, W Yang, SY Wong - Frontiers in computational …, 2021 - frontiersin.org
Cardiovascular diseases (CVDs) are the leading cause of death today. The current
identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a …
identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a …
HARDC: A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Deep learning-based models have achieved significant success in detecting cardiac
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
A hybrid deep CNN model for abnormal arrhythmia detection based on cardiac ECG signal
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …
Multi-scale convolutional neural network ensemble for multi-class arrhythmia classification
E Prabhakararao, S Dandapat - IEEE Journal of Biomedical …, 2021 - ieeexplore.ieee.org
The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early
diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and …
diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and …