Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 11; p. 3697
• Main Authors: Valerio, Andrea, Demarchi, Danilo, O’Flynn, Brendan, Motto Ros, Paolo, Tedesco, Salvatore
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.06.2024; MDPI
• Subjects: Adult; Algorithms; Blood pressure; Blood Pressure - physiology; Blood Pressure Determination - methods; Cognition - physiology; Coronary heart disease; cuffless blood pressure; Data collection; Datasets; Female; Humans; Hypertension; Machine Learning; Male; Neural networks; personalized health; photoplethysmogram; Photoplethysmography - methods; Physiology; pulse transit time; pulse wave analysis; Sensors; User interface; Veins & arteries; Workload; Young Adult; photoplethysmogram; pulse transit time; cuffless blood pressure; personalized health; pulse wave analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24113697

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.