Next Article in Journal
Self-Calibration for Star Sensors
Next Article in Special Issue
Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step
Previous Article in Journal
Investigation of Appropriate Scaling of Networks and Images for Convolutional Neural Network-Based Nerve Detection in Ultrasound-Guided Nerve Blocks
Previous Article in Special Issue
Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 24
Issue 11
10.3390/s24113697
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Andrea Valerio
1,*,
Danilo Demarchi
1,
Brendan O’Flynn
2,
Paolo Motto Ros
1,* and
Salvatore Tedesco
2
1
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
2
Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland
*
Authors to whom correspondence should be addressed.
Sensors 2024, 24(11), 3697; https://doi.org/10.3390/s24113697
Submission received: 2 May 2024 / Revised: 23 May 2024 / Accepted: 4 June 2024 / Published: 6 June 2024
(This article belongs to the Special Issue Wearable Technologies and Sensors for Healthcare and Wellbeing)
Browse Figures
Versions Notes

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.
Keywords: cuffless blood pressure; personalized health; photoplethysmogram; pulse transit time; pulse wave analysis cuffless blood pressure; personalized health; photoplethysmogram; pulse transit time; pulse wave analysis

Share and Cite

MDPI and ACS Style

Valerio, A.; Demarchi, D.; O’Flynn, B.; Motto Ros, P.; Tedesco, S. Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload. Sensors 2024, 24, 3697. https://doi.org/10.3390/s24113697

AMA Style

Valerio A, Demarchi D, O’Flynn B, Motto Ros P, Tedesco S. Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload. Sensors. 2024; 24(11):3697. https://doi.org/10.3390/s24113697

Chicago/Turabian Style

Valerio, Andrea, Danilo Demarchi, Brendan O’Flynn, Paolo Motto Ros, and Salvatore Tedesco. 2024. "Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload" Sensors 24, no. 11: 3697. https://doi.org/10.3390/s24113697

APA Style

Valerio, A., Demarchi, D., O’Flynn, B., Motto Ros, P., & Tedesco, S. (2024). Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload. Sensors, 24(11), 3697. https://doi.org/10.3390/s24113697

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop