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Yang, Sen, Zaki, Wan Suhaimizan Wan, Morgan, Stephen P., Cho, Siu-Yeung, Correia, Ricardo and Zhang, Yaping (2020) Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals. Optical and Quantum Electronics, 52 (3). ISSN 0306-8919

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Abstract

A novel method for the continual, cuff-less estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values based on signal complexity analysis of the photoplethysmogram (PPG) and the electrocardiogram (ECG) is reported. The proposed framework estimates the blood pressure (BP) values obtained from signals generated from 14 volunteers subjected to a series of exercise routines. Herein, the physiological signals were first pre-processed, followed by the extraction of complexity features from both the PPG and ECG. Subsequently the complexity features were used in regression models (artificial neural network (ANN), support vector machine (SVM) and LASSO) to predict the BP. The performance of the approach was evaluated by calculating the mean absolute error and the standard deviation of the predicted results and compared with the recommendations made by the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation. Complexity features from the ECG and PPG were investigated independently, along with the combined dataset. It was observed that the complexity features obtained from the combination of ECG and PPG signals resulted to an improved estimation accuracy for the BP. The most accurate DBP result of 5.15 ± 6.46 mmHg was obtained from ANN model, and SVM generated the most accurate prediction for the SBP which was estimated as 7.33 ± 9.53 mmHg. Results for DBP fall within recommended performance of the BHS but SBP is outside the range. Although initial results are promising, further improvements are required before the potential of this approach is fully realised.

Item Type:	Article
Keywords:	Blood pressure (BP); Complexity analysis; Photoplethysmogram (PPG); Electrocardiogram (ECG); Machine learning
Schools/Departments:	University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Architecture and Built Environment University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Electrical and Electronic Engineering
Identification Number:	https://doi.org/10.1007/s11082-020-2260-7
Depositing User:	Zhou, Elsie
Date Deposited:	23 Mar 2020 01:29
Last Modified:	23 Mar 2020 01:29
URI:	https://eprints.nottingham.ac.uk/id/eprint/60140

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