Artificial Intelligence and Machine Learning in Electrophysiology—a Short Review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cardiology and Cardiovascular Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11936-023-01004-4.pdf
Reference81 articles.
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2. Smith SW, Rapin J, Li J, et al. A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation. Int J Cardiol Heart Vasc. 2019;25:100423. https://doi.org/10.1016/j.ijcha.2019.100423.
3. • Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine. 2019;25(1):65–69. https://doi.org/10.1038/s41591-018-0268-3. Findings from this large 53,549 patient study displayed the ability of machine learning to detect and classify arrhythimas based on a single-lead EKG at a level of accuracy that outperformed the average cardiologist.
4. Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: a systematic review. Comput Biol Med. Jul 2020;122:103801. https://doi.org/10.1016/j.compbiomed.2020.103801.
5. Fotiadou E, Konopczynski T, Hesser J, Vullings R. End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising. Physiol Meas. 2020;41(1):015005. https://doi.org/10.1088/1361-6579/ab69b9.
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