Development of a personalized multiclass classification model to detect blood pressure variations associated with physical or cognitive workload
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Published version
Date
2024-06-06
Authors
Valerio, Andrea
Demarchi, Danilo
O'Flynn, Brendan
Motto Ros, Paolo
Tedesco, Salvatore
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Published Version
Abstract
Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.
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Keywords
Cuffless blood pressure , Personalized health , Photoplethysmogram , Pulse transit time , Pulse wave analysis
Citation
Valerio, A., Demarchi, D., O’Flynn, B., Motto Ros, P. and Tedesco, S. (2024) ‘Development of a personalized multiclass classification model to detect blood pressure variations associated with physical or cognitive workload’, Sensors, 24(11), p. 3697. Available at: https://doi.org/10.3390/s24113697