A fused electrocardiography arrhythmia detection method
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17410-6.pdf
Reference41 articles.
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2. Wang K, Yang G, Huang Y, Yin Y (2020) Multi-scale differential feature for ECG biometrics with collective matrix factorization. Pattern Recogn 102:107211. https://doi.org/10.1016/j.patcog.2020.107211
3. Moody GB, Mark RG (2001) The impact of the MIT-BIH Arrhythmia Database. IEEE Eng Med Biol Mag 20:45–50. https://doi.org/10.1109/51.932724
4. Yıldırım Ö, Pławiak P, Tan RS, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420. https://doi.org/10.1016/j.compbiomed.2018.09.009
5. Pławiak P (2018) Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 92:334–349. https://doi.org/10.1016/j.eswa.2017.09.022
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1. Pruned lightweight neural networks for arrhythmia classification with clinical 12-Lead ECGs;Applied Soft Computing;2024-03
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