Application of Independent Component Analysis and Nelder-Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation.
Sensors (Basel)
; 24(11)2024 May 30.
Article
em En
| MEDLINE
| ID: mdl-38894333
ABSTRACT
In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder-Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pressão Sanguínea
/
Determinação da Pressão Arterial
/
Algoritmos
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Taiwan