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 …
Artificial intelligence and machine learning in arrhythmias and cardiac electrophysiology
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of
intense exploration, showing potential to automate human tasks and even perform tasks …
intense exploration, showing potential to automate human tasks and even perform tasks …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
Transfer learning for ECG classification
K Weimann, TOF Conrad - Scientific reports, 2021 - nature.com
Remote monitoring devices, which can be worn or implanted, have enabled a more effective
healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor …
healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor …
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 …
Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection
Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will
seriously harm the life and health of patients. Traditional deep learning methods have weak …
seriously harm the life and health of patients. Traditional deep learning methods have weak …
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 …
[HTML][HTML] Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model
High-risk patients of cardiovascular disease can be provided with computerized
electrocardiogram (ECG) devices to detect Arrhythmia. These require long segments of …
electrocardiogram (ECG) devices to detect Arrhythmia. These require long segments of …
ECG signal classification based on deep CNN and BiLSTM
J Cheng, Q Zou, Y Zhao - BMC medical informatics and decision making, 2021 - Springer
Background Currently, cardiovascular disease has become a major disease endangering
human health, and the number of such patients is growing. Electrocardiogram (ECG) is an …
human health, and the number of such patients is growing. Electrocardiogram (ECG) is an …
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …