Automated heartbeat classification based on deep neural network with multiple input layers
H Shi, C Qin, D Xiao, L Zhao, C Liu - Knowledge-Based Systems, 2020 - Elsevier
The arrhythmia is an important group of cardiovascular disease. Electrocardiogram (ECG) is
commonly used for detecting arrhythmias. Computer-aided diagnosis system can diagnose …
commonly used for detecting arrhythmias. Computer-aided diagnosis system can diagnose …
An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators
R Anand, SV Lakshmi, D Pandey, BK Pandey - Evolving Systems, 2024 - Springer
Electrocardiogram (ECG) is one among the most common detecting techniques in the
analysis and detection of cardiac arrhythmia adopted due to its cost efficiency and simplicity …
analysis and detection of cardiac arrhythmia adopted due to its cost efficiency and simplicity …
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 …
ECG heartbeat arrhythmia classification using time-series augmented signals and deep learning approach
Electrocardiogram (ECG) signals are the best way to monitor the functionality and health of
the cardiovascular system and also identify ailments related to it. Abnormal heartbeats are …
the cardiovascular system and also identify ailments related to it. Abnormal heartbeats are …
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 …
Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification
Background Electrocardiogram (ECG) signal classification plays a critical role in the
automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be …
automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be …
SpEC: A system for patient specific ECG beat classification using deep residual network
Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac
disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG …
disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG …
Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss
TF Romdhane, MA Pr - Computers in Biology and Medicine, 2020 - Elsevier
The electrocardiogram (ECG) is an effective tool for cardiovascular disease diagnosis and
arrhythmia detection. Most methods proposed in the literature include the following steps: 1) …
arrhythmia detection. Most methods proposed in the literature include the following steps: 1) …
Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model
Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can
cause chest pain and trouble breathing, especially while active. However, some patients …
cause chest pain and trouble breathing, especially while active. However, some patients …
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 …