Rastegar, SolmazHosseini, Hamid GholamLowe, Andrew2026-05-202026-05-202023-01-22Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 23(3), 1259-. doi: 10.3390/s230312591424-82201424-8220http://hdl.handle.net/10292/21147<jats:p>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.</jats:p>© 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/).https://creativecommons.org/licenses/by/4.0/continuous blood pressureconvolutional neural network (CNN)electrocardiogram (ECG)photoplethysmography (PPG)4605 Data Management and Data Science46 Information and Computing SciencesMachine Learning and Artificial IntelligenceHeart DiseaseCardiovascularBioengineeringHypertensionCardiovascular3 Good Health and Well Being0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareHumansBlood PressurePhotoplethysmographyBlood Pressure DeterminationNeural Networks, ComputerElectrocardiographyHumansBlood Pressure DeterminationElectrocardiographyPhotoplethysmographyBlood PressureNeural Networks, ComputerHumansBlood PressurePhotoplethysmographyBlood Pressure DeterminationNeural Networks, ComputerElectrocardiographyJournal ArticleOpenAccess10.3390/s23031259