Optimized Blood Pressure Prediction With a Hybrid CNN-SVR Model Using Multiwavelength PPG

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 12
• Main Authors: Botrugno, Chiara, Dheman, Kanika, Bonazzi, Pietro, Dell'Olio, Francesco, Magno, Michele
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Accuracy; Adaptive optics; Biomedical monitoring; Blood pressure; Blood pressure (BP); Calibration; convolutional neural network (CNN); Convolutional neural networks; Estimation; Feature extraction; machine learning (ML); Monitoring; Optical sensors; photoplethysmography (PPG); support vector regression (SVR)
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2025.3599704

Noninvasive vital sign monitoring, especially blood pressure (BP), is crucial for evaluating overall health and identifying early indicators of medical issues. Photoplethysmography (PPG) is a noninvasive technique increasingly adopted in vital sign monitoring, as it allows for the continuous and precise measurement of cardiac-related signals. This technology is sensitive to fluctuations in blood volume within blood vessels, enabling real-time tracking of each heartbeat and identification of any irregularities. This study investigates the effectiveness of machine learning (ML) models for predicting arterial pressure based solely on PPG signals, utilizing a novel dataset and multiwavelength sensor technology that captures data from four distinct wavelengths: infrared (IR), red, green, and blue. The participant pool consisted of 88 individuals, with each measurement lasting 30 s at a sampling rate of 100 Hz. After a dedicated phase of signal preprocessing, three methodologies were assessed: a multilayer perceptron (MLP) utilizing 84 features per subject, a dimensionality reduction strategy through principal component analysis (PCA), and a convolutional neural network (CNN) architecture. The CNN approach showcased impressive performance metrics, with results aligning with the standards set by the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS). Additionally, to further enhance accuracy, a support vector regression (SVR) correction algorithm was applied, as a postprocessing phase, addressing discrepancies in measurements related to age, thereby improving the reliability of the arterial pressure predictions and reaching errors of <inline-formula> <tex-math notation="LaTeX">1.36~\pm ~2.98 </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">1.52~\pm ~2.67 </tex-math></inline-formula> mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. Compared with recent cuffless BP estimators that rely on single-wavelength PPG or pulse transit time (PTT) features and typically report mean absolute error (MAE) values of 3-5 mmHg, the proposed multiwavelength hybrid CNN-SVR shrinks the error by more than 60% (MAE =1.84/1.98 mmHg for SBP/DBP), while still meeting AAMI and BHS grade A/B requirements. This establishes, to the best of authors' knowledge, the first sub-2 mmHg, AAMI-compliant result obtained with a commodity optical front end and no calibration.