Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics
Link
https://link.springer.com/content/pdf/10.1007/s10462-022-10353-8.pdf
Reference317 articles.
1. Abderahman HN, Dajani HR, Bolic M, Groza VZ (2017) An integrated blood pressure measurement system for suppression of motion artifacts. Comput Methods Prog Biomed 145:1–10
2. Abrar S, Loo CK, Kubota N, Tahir GA (2020) A personalised blood pressure prediction system using Gaussian mixture regression and online recurrent extreme learning machine. In: 2020 international symposium on community-centric systems (CcS), pp 1–7. IEEE
3. Aguirre N, Grall-Maës E, Cymberknop LJ, Armentano RL (2021) Blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism. Sensors 21(6):2167
4. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322
5. Ahmad S, Chen S, Soueidan K, Batkin I, Bolic M, Dajani H, Groza V (2012) Electrocardiogram-assisted blood pressure estimation. IEEE Trans Biomed Eng 59(3):608–618
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