Enhancement of blood pressure estimation method via machine learning
Published 2021 View Full Article
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Title
Enhancement of blood pressure estimation method via machine learning
Authors
Keywords
Blood pressure estimation, Non-invasive, Machine learning
Journal
Alexandria Engineering Journal
Volume 60, Issue 6, Pages 5779-5796
Publisher
Elsevier BV
Online
2021-06-08
DOI
10.1016/j.aej.2021.04.035
References
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- Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges
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