Using Kalman filtering to analyze oscillometric blood pressure waveforms

Abstract

Measuring the blood pressure as accurately as possible can save a lot of human lives. Hence, it is very important to find an optimal method to determine the systolic and diastolic pressures out of the measured oscillometric blood pressure waveform. Recently, studies have been showing that, by working in the frequency domain, outperforming results could be obtained. Using the digital Taylor-Fourier transform (DTFT) even allows separating the breathing and cardiac activity that is present in the oscillometric waveform. Furthermore, an estimate of the frequency fluctuation can easily be obtained. In this paper, we will investigate whether or not a Kalman filtering implementation can provide better results than the DTFT analysis. In theory both approaches should be equally performing. Both techniques will be compared on measured oscillometric waveforms. Even if the alternating Kalman filter does not excel the DTFT algorithm in interharmonic rejection, it offers interesting signal decomposition alternatives.