Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
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] 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 …
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 …
[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 …
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
HV Denysyuk, RJ Pinto, PM Silva, RP Duarte… - Heliyon, 2023 - cell.com
The prevalence of cardiovascular diseases is increasing around the world. However, the
technology is evolving and can be monitored with low-cost sensors anywhere at any time …
technology is evolving and can be monitored with low-cost sensors anywhere at any time …
[HTML][HTML] 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 …
An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)
Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm.
Early and accurate detection is crucial for effective treatment. However, single-lead …
Early and accurate detection is crucial for effective treatment. However, single-lead …