IEEE J Biomed Health Inform. 2023 Jul;27(7):3622-3632. doi: 10.1109/JBHI.2023.3268886. Epub 2023 Jun 30.
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.
一种新的时间卷积网络（TCN）模型被用于从桡动脉血压波形重建中心主动脉血压（aBP）波形。该方法不需要像传统的传递函数方法那样进行手动特征提取。该方法利用 SphygmoCor CVMS 设备在 1032 名参与者中获得的数据作为测量数据库和 4374 名虚拟健康受试者的公共数据库，比较了 TCN 模型与已发表的卷积神经网络和双向长短期记忆（CNN-BiLSTM）模型的准确性和计算成本。在均方根误差（RMSE）方面，将 TCN 模型与 CNN-BiLSTM 进行了比较。TCN 模型在准确性和计算成本方面普遍优于现有的 CNN-BiLSTM 模型。对于测量数据库和公共数据库，TCN 模型的波形 RMSE 分别为 0.55 ± 0.40mmHg 和 0.84 ± 0.29mmHg。对于整个训练集，TCN 模型的训练时间分别为 9.63 分钟和 25.51 分钟；对于测量数据库和公共数据库，平均测试时间分别约为 1.79ms 和 8.58ms，每个测试脉搏信号。TCN 模型对于处理长输入信号既准确又快速，为测量 aBP 波形提供了一种新方法。这种方法可能有助于心血管疾病的早期监测和预防。
