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 …
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 …
[HTML][HTML] Visualizing the impact of feature attribution baselines
Path attribution methods are a gradient-based way of explaining deep models. These
methods require choosing a hyperparameter known as the baseline input. What does this …
methods require choosing a hyperparameter known as the baseline input. What does this …
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] 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 …
Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network
W Cai, Y Chen, J Guo, B Han, Y Shi, L Ji… - Computers in biology …, 2020 - Elsevier
Atrial fibrillation (AF) is the most common heart arrhythmia, and 12-lead electrocardiogram
(ECG) is regarded as the gold standard for AF diagnosis. Highly accurate diagnosis of AF …
(ECG) is regarded as the gold standard for AF diagnosis. Highly accurate diagnosis of AF …
A deep learning approach for atrial fibrillation classification using multi-feature time series data from ecg and ppg
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …
patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and …
Detection of atrial fibrillation using a machine learning approach
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings
Objective: We aim to combine deep neural networks and engineered features (hand-crafted
features based on medical domain knowledge) for cardiac arrhythmia detection from short …
features based on medical domain knowledge) for cardiac arrhythmia detection from short …
[HTML][HTML] Multi-classification neural network model for detection of abnormal heartbeat audio signals
H Malik, U Bashir, A Ahmad - Biomedical Engineering Advances, 2022 - Elsevier
Nowadays, heart disease is the leading cause of death. The high mortality rate and
escalating occurrence of heart diseases worldwide warrant the requirement for a fast and …
escalating occurrence of heart diseases worldwide warrant the requirement for a fast and …