Combined ECG Analysis Approach Based on Detection of Deviation from the Personal or Population Norm Using Wearable Devices
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3289-0_16
Reference16 articles.
1. Han C, Song Y, Lim H, Tae Y, Jang J, Lee B et al (2021) Automated detection of acute myocardial infarction using asynchronous electrocardiogram signals—preview of implementing artificial intelligence with multichannel electrocardiographs obtained from smartwatches: retrospective study. J Med Internet Res 23(9):e31129. https://doi.org/10.2196/31129
2. Shameer K, Johnson K, Glicksberg B, Dudley J, Sengupta P (2018) Machine learning in cardiovascular medicine: are we there yet? Heart 104(14):1156–1164. https://doi.org/10.1136/heartjnl-2017-311198
3. Han C, Kang K, Kim T, Uhm J, Park J, Jung I et al (2022) Artificial intelligence-enabled ECG algorithm for the prediction of coronary artery calcification. Front Cardiovascular Med 9. https://doi.org/10.3389/fcvm.2022.849223
4. Medina-Inojosa KJ, Noseworthy P, Rodeheffer R, Lopez-Jimenez F, Attia Z et al (2021) Artificial intelligence–augmented electrocardiogram detection of left ventricular systolic dysfunction in the general population Mayo Clinic Proc 96(10):2576–2586. https://doi.org/10.1016/j.mayocp.2021.02.029
5. Chaikovsky AP (2023) Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of AI anomaly detection. In: Qaisar SM, Nisar H, Subasi A (eds) Advances in non-invasive biomedical signal sensing and processing with machine learning. Springer, Cham. https://doi.org/10.1007/978-3-031-23239-8_5
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