Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform

Pooi Khoon Lim, Siew Cheok Ng, Wissam A. Jassim, Stephen J. Redmond, Mohammad Zilany, Alberto Avolio, Einly Lim*, Maw Pin Tan, Nigel H. Lovell

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)
    80 Downloads (Pure)

    Abstract

    We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = −0.3 ± 5.8 mmHg; SVR and −0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = −1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.

    Original languageEnglish
    Pages (from-to)14142-14161
    Number of pages20
    JournalSensors
    Volume15
    Issue number6
    DOIs
    Publication statusPublished - 16 Jun 2015

    Bibliographical note

    Copyright the Author(s) 2015. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • oscillometric blood pressure estimati on
    • multiple linear regression
    • support vector regression

    Fingerprint

    Dive into the research topics of 'Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform'. Together they form a unique fingerprint.

    Cite this