Key result
An artificial neural network estimating systolic blood pressure from PPG signals achieved a Mean Absolute Error of 3.85 mmHg and a Standard Deviation of 4.29 mmHg, meeting AAMI and BHS standards.
Why the study?
Existing cuff-based blood pressure methods cannot provide continuous monitoring, which is needed to track impaired autonomic control and reduce cardiovascular risk.
Can an artificial neural network accurately estimate continuous systolic blood pressure from photoplethysmographic signals compared to standard cuff-based methods?
Can an artificial neural network accurately estimate continuous systolic blood pressure from photoplethysmographic signals compared to standard cuff-based methods?
An artificial neural network can accurately estimate systolic blood pressure from PPG signals in a static position, meeting established clinical validation standards.
Answers are grounded in this paper and cite their sources.
Supports PPG-based cuffless BP estimation; leaves open prospective validation before clinical adoption.
High blood pressure is one of the most important precursors for Cardiovascular Diseases (CVDs), the most common cause of death in 2020, as reported by the World Health Organization (WHO). Moreover, many patients affected by neurodegenerative diseases (e.g., Parkinson’s Disease) exhibit impaired autonomic control, with inversion of the normal circadian arterial pressure cycle, and consequent augmented cardiovascular and fall risk. For all these reasons, a continuous pressure monitoring of these patients could represent a significant prognostic factor, and help adjusting their therapy. However, the existing cuff-based methods cannot provide continuous blood pressure readings. Our work is inspired by the newest approaches based on the photoplethysmographic (PPG) signal only, which has been used to continuously estimate systolic blood pressure (SP), using artificial neural networks (ANN), in order to create more compact and wearable devices. Our first database was derived from the PhysioNet resource; we extracted PPG and arterial blood pressure (ABP) signals, collected at a sampling frequency of 125 Hz, in a hospital environment. It consists of 249,672 PPG periods and the relative SP values. The second database was collected at STMicroelectronics s.r.l., in Agrate Brianza, using the MORFEA3 wearable device and a digital cuff-based sphygmomanometer, as reference. The pre-processing phase, in order to remove noise and motion artifacts and to segment the signal into periods, was carried out on Matlab R2019b. The noise removal was one of the challenging parts of the study because of the inaccuracy of the PPG signal during everyday-life activity, and this is the reason why the MORFEA3 dataset was acquired in a controlled environment in a static position. Different solutions were implemented to choose the input features that best represent the period morphology. The first database was used to train the multilayer feed-forward neural network with a back-propagation model, whereas the second one was used to test it. The results obtained in this project are promising and match the Association for the Advancement of Medical Instruments (AAMI) and the British Hypertension Society (BHS) standards. They show a Mean Absolute Error of 3.85 mmHg with a Standard Deviation of 4.29 mmHg, under the AAMI standard, and reach the grade A under the BHS standard.
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Casadei et al. (2022) studied High blood pressure. Artificial neural network for systolic blood pressure estimation from PPG signals vs. Cuff-based sphygmomanometer and ABP signals was evaluated on Mean Absolute Error of systolic blood pressure estimation (MAE 3.85 mmHg (SD 4.29 mmHg)). An artificial neural network estimating systolic blood pressure from PPG signals achieved a Mean Absolute Error of 3.85 mmHg and a Standard Deviation of 4.29 mmHg, meeting AAMI and BHS standards.
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