Non-Invasive Blood Pressure Sensing via Machine Learning

Abstract

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.

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis

“…Feature selection is crucial for improving model efficiency by focusing on important features, reducing dimensionality, and ultimately improving the overall performance in machine learning tasks. In this work, the Minimum Redundancy Maximum Relevance (MRMR) algorithm [ 33 ] is utilised. MRMR identifies the most informative features for a given task by considering both their relevance to the target variable and their redundancy with respect to each other.…”

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

“…XGBoost outperformed NNs for both systolic and diastolic BP estimation, demonstrating that XGBoost, combined with selected features, can effectively estimate BP from PPG signals, adhering to clinical standards and guidelines. This paves the way for the development of wearable PPG devices integrated with ML for BP monitoring [ 13 ]. Another innovative approach involved using dual PPG sensors in a wristwatch, placed on the palmar and dorsal sides of the wrist, along with custom-made interface sensors to detect contact pressure and skin temperature.…”

“… Summary of the applications of AI in the risk prediction, diagnosis, management, and treatment of hypertension [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. …”

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

“…Table 6 illustrates this difficulty as the studies utilized different datasets and various machine learning algorithms. For example, Attivissimo et al [10] achieved a smaller MAE for both SBP and DBP. This disparity could be attributed to differences in the datasets and the inclusion of demographic features that were potentially influenced by the sensors used to measure the PPG signals.…”

“…Furthermore, Slapničar et al [9], Attivissimo et al [10], Kachuee et al [11], Omer et al [12], Kachuee et al [13], and Liu et al [14] presented BP estimation systems utilizing PPG databases such as MIMIC II and MIMIC III. They employed various preprocessing and feature extraction techniques, including the use of first and second derivatives, maximal overlap discrete wavelet transform (MODWT), pulse transit time, and wavelet scattering transform (WST).…”