Oscillometric blood pressure estimation using machine learning-based mapping of waveform features

Hypertension influences cardiovascular diseases, such as heart attacks and strokes. Blood pressure (BP) monitoring is essential for detecting hypertension and assessing its consequences. BP was traditionally measured using stethoscopes and pressure cuffs, which had several limitations. Additionally, automated blood pressure machines are not always accurate. Blood pressure measurement can be conducted more accurately and sensitively through a novel, non-invasive, and automated method. In this paper, a hybrid classification-mapping model is proposed to estimate Systolic (SBP) and Diastolic (DBP) blood pressure using 155 subjects from the University of New South Wales non-invasive BP (NIBP) dataset. In addition to exploring new beat-related features derived from oscillometric waveforms (OW), our study employs eight distinct feature ranking techniques to optimize the performance of different machine learning classifiers (K Nearest Neighbor (KNN), Ensemble KNN, Ensemble Bagged Tree, and Support Vector Machine (SVM)). As a comparison to existing methods for estimating DBP, which report a Mean Absolute Error (MAE) of 3.42 ± 5.38 mmHg, our approach achieves remarkably comparable results for estimating SBP, with an MAE of 1.28 ± 2.27 mmHg. Considering our promising results, implementing our methodology could provide a more reliable and convenient way to monitor blood pressure via remote healthcare.