Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals
Author:
Funder
Natural Science Foundation of Fujian Province
Publisher
Springer Science and Business Media LLC
Subject
Cognitive Neuroscience
Link
https://link.springer.com/content/pdf/10.1007/s11571-022-09870-7.pdf
Reference101 articles.
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2. Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl-Based Syst 132:62–71
3. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396
4. Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 405:81–90
5. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415:190–198
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review;Computers in Biology and Medicine;2024-04
2. An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning;Physiological Measurement;2024-03-01
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