[HTML][HTML] Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for
cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the …
cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the …
Lightx3ecg: A lightweight and explainable deep learning system for 3-lead electrocardiogram classification
Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one
of the most serious dangers to human health, and the number of such patients is still …
of the most serious dangers to human health, and the number of such patients is still …
Interpretation and classification of arrhythmia using deep convolutional network
P Singh, A Sharma - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
Electrocardiogram (ECG) signal analysis can be time-consuming, tedious, and error-prone.
Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac …
Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac …
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 …
Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification
Deep learning (DL) has achieved promising performance in detecting common
abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy …
abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy …
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 …
A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge …
M Sepahvand, F Abdali-Mohammadi - Information Sciences, 2022 - Elsevier
Deep learning models developed through multi-lead electrocardiogram (ECG) signals are
considered the leading methods for the automated detection of arrhythmia on computer …
considered the leading methods for the automated detection of arrhythmia on computer …
Usefulness of machine learning-based detection and classification of cardiac arrhythmias with 12-lead electrocardiograms
Background Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify
different types of cardiac arrhythmias with the use of a single-lead ECG input data set have …
different types of cardiac arrhythmias with the use of a single-lead ECG input data set have …
Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model
Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs).
They are also being used to develop computer-assisted methods for heart disease …
They are also being used to develop computer-assisted methods for heart disease …
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Background and objective Cardiac arrhythmia, which is an abnormal heart rhythm, is a
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …