Development of a web-based system for interfacing a portable Bluetooth vital sign monitor

Palma, Luisa De; Attivissimo, Filippo; Nisio, Attilio Di; Lanzolla, Anna Maria Lucia; Ragolia, Mattia Alessandro; Spadavecchia, Maurizio

doi:10.1109/memea54994.2022.9856526

Cited by 5 publications

(2 citation statements)

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“…The maximum depth of a tree is used to control over-fitting as higher depth will make the model more complex and more likely to overfit; the value 0 is only accepted in a loss-guided growing policy while large values bring an aggressive consumption of memory. Any positive value is admissible, with typical values in [3,10]; in this work, trial and error was used to modify the upper bound of the range to obtain better results. The subsample is, instead, the fraction of observations to be randomly sampled for each tree and is useful to prevent overfitting; in fact, lower values make the algorithm more conservative while too small values might lead to under-fitting.…”

Section: Xgboost Modelsmentioning

confidence: 99%

“…For this reason, there is the spread of the development of practical and reliable telemedicine solutions [1][2][3][4] to guarantee monitoring at home and at hospital with the aim of ensuring early identification and prevention of cardiovascular diseases, hypertension, and other related diseases. As concerns BP measurement, traditional cuff-based devices have several disadvantages because they are not always accurate, they need appropriate calibration, and they do not allow continuous monitoring since performing a measurement requires about one minute or more.…”

Section: Introductionmentioning

confidence: 99%

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Non-Invasive Blood Pressure Sensing via Machine Learning

Attivissimo,

D’Alessandro,

De Palma

et al. 2023

Sensors

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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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Section: Xgboost Modelsmentioning

confidence: 99%