Using Bayesian Optimization and Wavelet Decomposition in GPU for Arterial Blood Pressure Estimation.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 1012-1015, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-36086463
ABSTRACT
Continuous monitoring of arterial blood pressure (ABP) of patients in hospital is currently carried out in an invasive way, which could represent a risk for them. In this paper, a noninvasive methodology to optimize ABP estimators using electrocardiogram and photoplethysmography signals is proposed. For this, the XGBoost machine learning model, optimized with Bayesian techniques, is executed in a Graphics Processing Unit, which drastically reduces execution time. The methodology is evaluated using the MIMIC-III Waveform Database. Systolic and diastolic pressures are estimated with mean absolute error values of 15.85 and 11.59 mmHg, respectively, similar to those of the state of the art. The main advantage of the proposed methodology with respect to others of the current state of the art is that it allows the optimization of the estimator model to be performed automatically and more efficiently at the computational level for the data available. Clinical Relevance- This approach has the advantage of using noninvasive methods to continuously monitor patient's arterial blood pressure, reducing the risk for patients.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Determinación de la Presión Sanguínea
/
Presión Arterial
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2022
Tipo del documento:
Article