A novel temporal convolutional network (TCN) model is utilized to reconstruct the central aortic blood pressure (aBP) waveform from the radial blood pressure waveform. The method does not need manual feature extraction as traditional transfer function approaches. The data acquired by the SphygmoCor CVMS device in 1,032 participants as a measured database and a public database of 4,374 virtual healthy subjects were used to compare the accuracy and computational cost of the TCN model with the published convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model. The TCN model was compared with CNN-BiLSTM in the root mean square error (RMSE). The TCN model generally outperformed the existing CNN-BiLSTM model in terms of accuracy and computational cost. For the measured and public databases, the RMSE of the waveform using the TCN model was 0.55 ± 0.40 mmHg and 0.84 ± 0.29 mmHg, respectively. The training time of the TCN model was 9.63 min and 25.51 min for the entire training set; the average test time was around 1.79 ms and 8.58 ms per test pulse signal from the measured and public databases, respectively. The TCN model is accurate and fast for processing long input signals, and provides a novel method for measuring the aBP waveform. This method may contribute to the early monitoring and prevention of cardiovascular disease.

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 () 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 ( ), the correlation coefficient between the and the measured PTT () (mean: ; beat-to-beat: ) is higher than that between the PTT estimated by LR ( ) and (mean: ; beat-to-beat: ). The standard deviation (SD) of the difference between the and ( ; beat-to-beat: ) is significantly less than that between the and (; beat-to-beat: 10 ms), but no significant difference exists between their mean ( ). The lack of frequency features of radial pressure waveform caused obvious reduction in the correlation coefficient and SD of the difference between the and . 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.