A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms
A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms
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DOI：
10.1016/j.cmpb.2021.106191
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发表时间：
2021-05-30
影响因子：
6.1
通讯作者：
Atkinson, Ian
中科院分区：
文献类型：
--
作者：
Baker, Stephanie;Xiang, Wei;Atkinson, Ian
背景和目的：持续和无创的血压监测将使医疗保健发生革命性的变化。目前，血压(BP)只能使用基于袖带的刺激性设备或有创性动脉内监测来准确监测。在这项工作中，我们提出了一种新的混合神经网络来精确估计血压(BP)，只使用无创心电(ECG)和光体积图(PPG)波形作为输入。方法：提出了一种混合神经网络，该网络结合了时间卷积层的特征检测能力和对长短期记忆层提供的序列数据的强大性能。原始的心电信号和光体积图波形被连接在一起作为网络输入。该网络是使用TensorFlow框架开发的。根据英国高血压学会(BHS)和医疗器械促进协会(AAMI)制定的著名标准，对我们的方案进行了分析并与文献进行了比较。结果：我们的方案获得了极低的平均绝对误差(MAE)，SBP为4.41 mm Hg，DBP为2.91 mm Hg，MAP为2.77 mm Hg。Bland Altman和回归图显示了我们的方案与黄金标准动脉内监测之间的强烈一致性。此外，我们的方案还满足AAMI制定的BP设备标准。我们还根据BP设备的BHS协议概述的标准获得了A级。结论：我们的CNN-LSTM网络优于目前最先进的无创血压测量方案，该方案可以从PPG和心电波形中进行血压测量。这些结果提供了一种有效的机器学习方法，可以很容易地实施到非侵入性可穿戴设备中，用于持续的临床和家庭监测。(C)2021年提交人。由爱思唯尔出版。这是一篇基于CC by License(http://creativecommons.org/licenses/by/4.0/)的开放获取文章
Background and objectives: Continuous and non-invasive blood pressure monitoring would revolutionize healthcare. Currently, blood pressure (BP) can only be accurately monitored using obtrusive cuff-based devices or invasive intra-arterial monitoring. In this work, we propose a novel hybrid neural network for the accurate estimation of blood pressure (BP) using only non-invasive electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms as inputs. Methods: This work proposes a hybrid neural network combines the feature detection abilities of temporal convolutional layers with the strong performance on sequential data offered by long short-term memory layers. Raw electrocardiogram and photoplethysmogram waveforms are concatenated and used as network inputs. The network was developed using the TensorFlow framework. Our scheme is analysed and compared to the literature in terms of well known standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Results: Our scheme achieves extremely low mean absolute errors (MAEs) of 4.41 mmHg for SBP, 2.91 mmHg for DBP, and 2.77 mmHg for MAP. A strong level of agreement between our scheme and the gold standard intra-arterial monitoring is shown through Bland Altman and regression plots. Additionally, the standard for BP devices established by AAMI is met by our scheme. We also achieve a grade of 'A' based on the criteria outlined by the BHS protocol for BP devices. Conclusions: Our CNN-LSTM network outperforms current state-of-the-art schemes for non-invasive BP measurement from PPG and ECG waveforms. These results provide an effective machine learning approach that could readily be implemented into non-invasive wearable devices for use in continuous clinical and at-home monitoring. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )