Optimized deep neural network models for blood pressure classification using Fourier analysis-based time–frequency spectrogram of photoplethysmography signal-Reference-Cited by-同舟云学术

Optimized deep neural network models for blood pressure classification using Fourier analysis-based time–frequency spectrogram of photoplethysmography signal

Published:2023-06-24 Issue:4 Volume:13 Page:739-750
ISSN:2093-9868
Container-title:Biomedical Engineering Letters
language:en
Short-container-title:Biomed. Eng. Lett.

Author:

Pankaj ,Kumar Ashish,Kumar Manjeet^ORCID,Komaragiri Rama

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13534-023-00296-6.pdf

Reference51 articles.

1. Health topic, cardiovascular disease, world health organization. 2023. https://www.who.int/health-topics/hypertension. Accessed 31 Mar 2023.

2. Hypertension, key facts, world health organization. 2023. https://www.who.int/news-room/fact-sheets/detail/hypertension. Accessed 31 Mar 2023.

3. Meyerovitz CV, et al. Social determinants, blood pressure control, and racial inequities in childbearing age women with hypertension, 2001 to 2018. J Am Heart Assoc. 2023;12(5):e027169.

4. Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75:285–92.

5. Song K, Chung KY, Chang JH. Cuffless deep learning-based blood pressure estimation for smart wristwatches. IEEE Trans Instrum Meas. 2020;69(7):4292–302.

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1. Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework;Physical and Engineering Sciences in Medicine;2023-09-25