Deep learning in ECG diagnosis: A review
X Liu, H Wang, Z Li, L Qin - Knowledge-Based Systems, 2021 - Elsevier
Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …
[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 …
Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets
Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related
complications that can increase the risk of strokes and heart failure. Manual …
complications that can increase the risk of strokes and heart failure. Manual …
Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
This article presents a new deep learning approach for cardiac arrhythmia (17 classes)
detection based on long-duration electrocardiography (ECG) signal analysis …
detection based on long-duration electrocardiography (ECG) signal analysis …
Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Deep learning models have become a popular mode to classify electrocardiogram (ECG)
data. Investigators have used a variety of deep learning techniques for this application …
data. Investigators have used a variety of deep learning techniques for this application …
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data
HM Rai, K Chatterjee - Applied Intelligence, 2022 - Springer
Automatic and accurate prognosis of myocardial infarction (MI) using electrocardiogram
(ECG) signals is a challenging task for the diagnosis and treatment of heart diseases. MI is …
(ECG) signals is a challenging task for the diagnosis and treatment of heart diseases. MI is …
Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant
interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as …
interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as …