Assessing the Feasibility of Cuffless Pulse Wave Velocity Measurement: A Preliminary Study Using Bioimpedance and Sphygmomanometer

Hammoud, Ahmad; Amaliya, Latypova; Tikhomirov, Alexey; Safronova, Tatiana; Briko, Andrey; Pokrovskaya, Anna

doi:10.1109/stdh59314.2023.10490638

2023 Systems and Technologies of the Digital HealthCare (STDH) 2023

DOI: 10.1109/stdh59314.2023.10490638

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Assessing the Feasibility of Cuffless Pulse Wave Velocity Measurement: A Preliminary Study Using Bioimpedance and Sphygmomanometer

Ahmad Hammoud,

Latypova Amaliya,

Alexey Tikhomirov

et al.

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2024

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Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm

Gao,

Chen,

et al. 2024

Sci Rep

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Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.

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Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm

Gao,

Chen,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Assessing the Feasibility of Cuffless Pulse Wave Velocity Measurement: A Preliminary Study Using Bioimpedance and Sphygmomanometer

Cited by 1 publication

References 21 publications

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm

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