Estimation of pulse transit time from radial pressure waveform alone by artificial neural network

Hanguang Xiao, Mark Butlin, Isabella Tan, Ahmad Qasem, Alberto Avolio

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)


    Objective: To validate the feasibility of the estimation of pulse transit time (PTT) by artificial neural network (ANN) from radial pressure waveform alone. Methods: A cascade ANN with ten-fold cross validation was applied to invasively and simultaneously recorded aortic and radial pressure waveforms during rest and nitroglycerin infusion (n=62) for the estimation of mean and beat-to-beat PTT. The results of the ANN models were compared to a multiple linear regression (LR) model when the features of radial arterial pressure waveform in time and frequency domains were used as the predictors of the models. Results: For the estimation of mean PTT and beat-to-beat PTT by ANN (PTTANN), the correlation coefficient between the PTTANN and the measured PTT (PTTmeasured) (mean: r=0.73; beat-to-beat: r=0.85) is higher than that between the PTT estimated by LR (PTTLR) and PTTmeasured (mean: r=0.46; beat-to-beat: r=0.79). The standard deviation (SD) of the difference between the PTTANN and PTTmeasured (mean: SD=10 ms; beat-to-beat: SD=8 ms) is significantly less than that between the PTTLR and PTTmeasured (mean: SD=17 ms; beat-to-beat: 10 ms), but no significant difference exists between their mean (p=0.92). The lack of frequency features of radial pressure waveform caused obvious reduction in the correlation coefficient (Δr=0.16) and SD of the difference (ΔSD=6 ms) between the PTTANN and PTTmeasured. The performance of the ANN was improved by increasing the sample number but not by increasing the neuron number. Conclusion: ANN is a potential method of PTT estimation from a single pressure measurement at radial artery.

    Original languageEnglish
    Pages (from-to)1140-1147
    Number of pages8
    JournalIEEE Journal of Biomedical and Health Informatics
    Issue number4
    Early online date31 Aug 2017
    Publication statusPublished - Jul 2018


    • arterial pressure
    • artificial neural network
    • pulse wave velocity
    • pulse transit time


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