Higher Order Spectra-Based Ensemble Learning Approach for Cuffless Blood Pressure Estimation

Abstract

Cuffless blood pressure (BP) estimation has emerged as an alternative and effective technique to mitigate the limitations of conventional sphygmomanometers for prolonged BP monitoring. Cuffless BP can be estimated from cardiovascular measurements, including photoplethysmogram and electrocardiogram signals. Several machine learning-based BP estimation methods are available in the literature. However, the effectiveness of higher order spectral features, such as bispectrum, bicoherence, and trispectrum, for BP estimation has never been explored. This letter proposes efficient ensemble learning-based approaches for cuffless BP estimation utilizing the higher order spectrum of the cardio signals. The extracted higher order spectral features are incorporated in ensemble learning-based extra trees and categorical boosting models. These methods incorporate multiple weak learners to produce the desired estimates. The novel features capture the nonlinear interactions and phase coupling between different frequency components. The proposed techniques are validated using various international standards for cuffless BP estimation tasks. The experimental results demonstrate that the proposed methods outperform state-of-the-art techniques. Furthermore, the proposed machine learning models are executed on the Xilinx PYNQ-Z2 board to verify the hardware compatibility.