Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and GMM-HMM Classification Approach

This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted from auscultatory waveforms (AWs) and using a Gaussian Mixture Models and Hidden Markov Model (GMM-HMM) classification approach. The three time domain features selected include the cuff pressure (CP), the energy of the Korotkoff pulses (KE), and the slope of the KE (SKE). The proposed GMM-HMM can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. We conclude that the proposed GMM-HMM estimation method is a very promising method improving the accuracy of automated non-invasive measurement of blood pressure.