[HTML][HTML] A review on deep learning methods for ECG arrhythmia classification
Z Ebrahimi, M Loni, M Daneshtalab… - Expert Systems with …, 2020 - Elsevier
Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
[PDF][PDF] Arrhythmia modern classification techniques: A review
M Saber, M Abotaleb - J. Artif. Intell. Metaheuristics, 2022 - researchgate.net
Artificial intelligence methods are utilized in biological signal processing to locate and
extract interesting data. The examination of ECG signal characteristics is crucial for the …
extract interesting data. The examination of ECG signal characteristics is crucial for the …
Arrhythmia classification techniques using deep neural network
Electrocardiogram (ECG) is the most common and low‐cost diagnostic tool used in
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …
[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
Atrial fibrillation detection using a feedforward neural network
Purpose In this study, we aimed to develop an automatic atrial fibrillation detection
technique for the early prediction of atrial fibrillation, that can be used with wearable devices …
technique for the early prediction of atrial fibrillation, that can be used with wearable devices …
Integration of results from convolutional neural network in a support vector machine for the detection of atrial fibrillation
C Ma, S Wei, T Chen, J Zhong, Z Liu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Atrial fibrillation (AF) can cause a variety of heart diseases and its detection is insufficient in
outside hospital. We proposed three methods for AF diagnosis in ambulatory settings. The …
outside hospital. We proposed three methods for AF diagnosis in ambulatory settings. The …
Study of the few-shot learning for ECG classification based on the PTB-XL dataset
The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists
of P, QRS, and T waves. Information provided from the signal based on the intervals and …
of P, QRS, and T waves. Information provided from the signal based on the intervals and …
A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms
Objective: Electrocardiography is the most common tool to diagnose cardiovascular
diseases. Annotation, segmentation and rhythm classification of ECGs are challenging …
diseases. Annotation, segmentation and rhythm classification of ECGs are challenging …
Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN
Atrial fibrillation (AF) is the most common type of sustained cardiac arrhythmia, and is
associated with stroke, coronary artery disease and mortality. Thus, early detection is crucial …
associated with stroke, coronary artery disease and mortality. Thus, early detection is crucial …
DDCNN: A deep learning model for AF detection from a single-lead short ECG signal
With the popularity of the wireless body sensor network, real-time and continuous collection
of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data …
of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data …