Key result
Temporal convolutional network outperforms CNN-BiLSTM to reconstruct central aortic blood pressure waveforms with ~0.55 mmHg error.
Why the study?
Traditional transfer function approaches require manual feature extraction to reconstruct the central aortic blood pressure waveform from the radial blood pressure waveform.
Does a temporal convolutional network (TCN) model improve the accuracy and computational cost of estimating central aortic blood pressure waveforms compared to a CNN-BiLSTM model?
Does a temporal convolutional network (TCN) model improve the accuracy and computational cost of estimating central aortic blood pressure waveforms compared to a CNN-BiLSTM model?
The TCN model provides an accurate and fast method for reconstructing central aortic blood pressure waveforms from radial waveforms without manual feature extraction.
May enable real-time noninvasive central pressure monitoring; leaves open prospective outcome validation before clinical use.
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.
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Liu et al. (2023) studied this question. Temporal convolutional network (TCN) model vs. CNN-BiLSTM model was evaluated on Root mean square error (RMSE) of the reconstructed waveform. A temporal convolutional network model accurately reconstructed central aortic blood pressure waveforms with an RMSE of 0.55 ± 0.40 mmHg in measured data, outperforming the CNN-BiLSTM model.
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