Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China; Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei University of Technology, Hefei, 230009, China.
Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China.
Comput Biol Med. 2021 Nov;138:104877. doi: 10.1016/j.compbiomed.2021.104877. Epub 2021 Sep 21.
Cardiovascular disease (CVD) is one of the most serious diseases threatening human health. Arterial blood pressure (ABP) waveforms, containing vivid cardiovascular information, are of great significance for the diagnosis and the prevention of CVD. This paper proposes a deep learning model, named ABP-Net, to transform photoplethysmogram (PPG) signals into ABP waveforms that contain vital physiological information related to cardiovascular systems. In order to guarantee the quality of the predicted ABP waveforms, the structure of the network, the input signals and the loss functions are carefully designed. Specifically, a Wave-U-Net, one kind of fully convolutional neural networks (CNN), is taken as the core architecture of the ABP-Net. Besides the original PPG signals, its first derivative and second derivative signals are all utilized as the inputs of the ABP-Net. Additionally, the maximal absolute loss, accompany with the mean squared error loss is employed to ensure the match of the predicted ABP waveform with the reference one. The performance of the proposed ABP network is tested on the public MIMIC II database both in subject-dependent and subject-independent manners. Both results verify the superior performance of the proposed model over those existing methods accordingly. The mean absolute error (MAE) and the root-mean-square error (RMSE) between the predicted waveforms via the ABP-Net and the reference ones are 3.20 mmHg and 4.38 mmHg during the subject-dependent experiments while those are 5.57 mmHg and 7.15 mmHg during the subject-independent experiments. Benefiting from the predicted high-quality ABP waveforms, more ABP related physiological parameters can be better obtained, which effectively expands the application scope of PPG devices.
心血管疾病（CVD）是威胁人类健康的最严重疾病之一。动脉血压（ABP）波形包含丰富的心血管信息，对于 CVD 的诊断和预防具有重要意义。本文提出了一种深度学习模型，称为 ABP-Net，可将光电容积脉搏波（PPG）信号转换为包含与心血管系统相关的重要生理信息的 ABP 波形。为了保证预测 ABP 波形的质量，精心设计了网络结构、输入信号和损失函数。具体来说，采用一种全卷积神经网络（CNN）的 Wave-U-Net 作为 ABP-Net 的核心架构。除了原始的 PPG 信号外，还将其一阶导数和二阶导数信号作为 ABP-Net 的输入。此外，采用最大绝对值损失和均方误差损失来保证预测 ABP 波形与参考波形的匹配。在公共 MIMIC II 数据库中，通过在主体相关和主体无关的方式对提出的 ABP 网络的性能进行了测试。实验结果均验证了所提出的模型优于现有方法的性能。在主体相关实验中，通过 ABP-Net 预测的波形与参考波形之间的平均绝对误差（MAE）和均方根误差（RMSE）分别为 3.20mmHg 和 4.38mmHg，而在主体无关实验中，这两个指标分别为 5.57mmHg 和 7.15mmHg。受益于预测的高质量 ABP 波形，可以更好地获得更多与 ABP 相关的生理参数，从而有效地扩展了 PPG 设备的应用范围。
