A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms

Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2021) A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms. Computer Methods and Programs in Biomedicine, 207. 106191.

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Abstract

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.

Item ID: 68571
Item Type: Article (Research - C1)
ISSN: 1872-7565
Keywords: Cuffless blood pressure, Photoplethysmogram, Electrocardiogram, Machine learning, Neural networks, Wearable healthcare
Copyright Information: ©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)
Funders: Australian Government Research Training Scholarship
Date Deposited: 03 Aug 2021 02:11
FoR Codes: 40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified @ 50%
42 HEALTH SCIENCES > 4203 Health services and systems > 420309 Health management @ 50%
SEO Codes: 20 HEALTH > 2004 Public health (excl. specific population health) > 200407 Health status (incl. wellbeing) @ 100%
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