Key result
XGBoost model using PPG signals estimates blood pressure within ~6 mmHg.
Why the study?
The study was conducted to develop non-invasive machine learning methods for estimating blood pressure suitable for telemedicine healthcare monitoring.
Can machine learning models using photoplethysmography signals accurately estimate non-invasive blood pressure for telemedicine monitoring?
Can machine learning models using photoplethysmography signals accurately estimate non-invasive blood pressure for telemedicine monitoring?
An XGBoost machine learning model using photoplethysmography signals can accurately estimate blood pressure non-invasively, meeting established clinical standards for telemedicine applications.
Supports PPG-based BP estimation for telemedicine; leaves open prospective validation before clinical adoption.
In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.
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Attivissimo et al. (2023) studied Blood pressure estimation. XGBoost regression model using photoplethysmography (PPG) signals vs. Neural Network models and literature works was evaluated on Root Mean Square Error (RMSE) for systolic and diastolic blood pressure. An XGBoost regression model using photoplethysmography signals estimated systolic and diastolic blood pressure with an RMSE of 5.67 mmHg and 3.95 mmHg, respectively.
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