A computationally efficient CNN-LSTM neural network for estimation of blood pressure from features of electrocardiogram and photoplethysmogram waveforms

Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2022) A computationally efficient CNN-LSTM neural network for estimation of blood pressure from features of electrocardiogram and photoplethysmogram waveforms. Knowledge Based Systems. (In Press)

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Abstract

Continuous blood pressure (BP) monitoring would significantly improve diagnosis and treatment of hypertension. Current at-home monitoring relies on uncomfortable and unreliable cuff-based devices, which are incapable of continuous measurement. In this work, we present a new hybrid neural network (NN) that combines convolutional layers with long short-term memory (LSTM) layers to classify systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP), using 12 straightforward features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. Our proposed network achieves mean absolute errors (MAEs) of 4.53 mmHg, 3.37 mmHg and 3.36 mmHg for SBP, DBP and MAP respectively. Additionally, our scheme passes the criteria outlined by the Association for the Advancement of Medical Instrumentation (AAMI) and achieves an A grade in accordance with the British Hypertension Society (BHS) protocol. These results provide a deep learning approach to BP estimation that could be implemented in low-power wearable devices.

Item ID: 74366
Item Type: Article (Research - C1)
ISSN: 1872-7409
Keywords: Cuffless blood pressure; Neural network; Machine learning; Wearable technology; Electrocardiogram; Photoplethysmogram
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Copyright Information: © 2022 Elsevier B.V. All rights reserved.
Date Deposited: 01 Jun 2022 02:23
FoR Codes: 42 HEALTH SCIENCES > 4203 Health services and systems > 420309 Health management @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
20 HEALTH > 2004 Public health (excl. specific population health) > 200407 Health status (incl. wellbeing) @ 50%
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