An accurate hypertension detection model based on a new odd-even pattern using ballistocardiograph signals
Article
Article Title | An accurate hypertension detection model based on a new odd-even pattern using ballistocardiograph signals |
---|---|
ERA Journal ID | 32032 |
Article Category | Article |
Authors | Dogan, Sengul, Barua, Prabal Datta, Tuncer, Turker and Acharya, U. Rajendra |
Journal Title | Engineering Applications of Artificial Intelligence |
Journal Citation | 133 (Part D) |
Article Number | 108306 |
Number of Pages | 11 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0952-1976 |
1873-6769 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2024.108306 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0952197624004640 |
Abstract | Background Methods and architecture Results Conclusions |
Keywords | Ballistocardiograph signals; Hypertension detection; Machine learning; Self-organized feature engineering |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Firat University, Turkey |
School of Business | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z84qw/an-accurate-hypertension-detection-model-based-on-a-new-odd-even-pattern-using-ballistocardiograph-signals
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