Estimation of aortic systolic blood pressure from radial systolic and diastolic blood pressures alone using artificial neural networks

Hanguang Xiao, Ahmad Qasem, Mark Butlin, Alberto Avolio

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)

    Abstract

    Background: Current aortic SBP estimation methods require recording of a peripheral pressure waveform, a step with no consensus on method. This study investigates the possibility of aortic SBP estimation from radial SBP and DBP using artificial neural networks (ANN) with [ANNSBP.DBP.heart rate (HR)] and without HR (ANNSBP.DBP).

    Methods: Ten-fold cross validation was applied to invasive, simultaneously recorded aortic and radial pressure during rest and nitroglycerin infusion (n = 62 patients). The results of the ANN models were compared with an ANN model using additional waveform features (ANNwaveform), to an N-point moving average method (NPMA) and to existing, validated generalized transfer function (GTF).

    Results: Estimated aortic SBP for all methods was on average less than 1 mmHg away from measured aortic SBP with the exception of NPMA (difference 2.0 ± 3.5 mmHg, P = 0.62). Variability of the difference was significantly greater in ANNSBP.DBP.HR and ANNSBP.DBP (both SD of ± 5.9 mmHg, P < 0.001 compared with GTF, ± 4.0 mmHg, P < 0.001). Inclusion of waveform features decreased the variability (ANNwaveform ± 3.9 mmHg, P = 0.264). Estimated aortic SBP in all models was correlated with measured SBP, with ANN models providing statistically similar results to the GTF method, only the NPMA being statistically different (P = 0.031).

    Conclusion: These findings indicate that use of radial SBP, DBP, and HR alone can provide aortic SBP estimation comparable with the GTF, albeit with slightly greater variance. Pending noninvasive validation, the technique provides plausible aortic SBP estimation without waveform analysis.

    Original languageEnglish
    Pages (from-to)1577–1585
    Number of pages9
    JournalJournal of Hypertension
    Volume35
    Issue number8
    Early online date6 Mar 2017
    DOIs
    Publication statusPublished - Aug 2017

    Keywords

    • aortic pressure
    • cascade forward artificial neural network
    • generalized transfer function
    • N-point moving average

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