Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals
Date
Authors
Rastegar, Solmaz
Hosseini, Hamid Gholam
Lowe, Andrew
Supervisor
Item type
Journal Article
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MDPI AG
Abstract
Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 2.45 mmHg (MAE ± STD) for SBP and 3.08 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.Description
Keywords
continuous blood pressure, convolutional neural network (CNN), electrocardiogram (ECG), photoplethysmography (PPG), 4605 Data Management and Data Science, 46 Information and Computing Sciences, Machine Learning and Artificial Intelligence, Heart Disease, Cardiovascular, Bioengineering, Hypertension, Cardiovascular, 3 Good Health and Well Being, 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4104 Environmental management, 4606 Distributed computing and systems software
Source
Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 23(3), 1259-. doi: 10.3390/s23031259
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).