Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response.

Autor: Gonzalez-Carabarin L; Department of Electrical Engineering Eindhoven University of Technology, Groene Loper 19, AP Eindhoven 5612, The Netherlands. Electronic address: l.gonzalez.carabarin@tue.nl., Castellanos-Alvarado EA; Research Centre for Applied Sciences and Engineering (CUCEI), University of Guadalajara, Blvd. Marcelino Garcia Barragan 1421, Guadalajara, 44430, Jalisco, Mexico., Castro-Garcia P; Research Centre for Applied Sciences and Engineering (CUCEI), University of Guadalajara, Blvd. Marcelino Garcia Barragan 1421, Guadalajara, 44430, Jalisco, Mexico., Garcia-Ramirez MA; Research Centre for Applied Sciences and Engineering (CUCEI), University of Guadalajara, Blvd. Marcelino Garcia Barragan 1421, Guadalajara, 44430, Jalisco, Mexico. Electronic address: mario.garcia@academicos.udg.mx.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2021 Sep; Vol. 209, pp. 106314. Date of Electronic Publication: 2021 Aug 08.
DOI: 10.1016/j.cmpb.2021.106314
Abstrakt: Stress appears as a response for a broad variety of physiological stimuli. It does vary among individuals in amplitude, phase and frequency. Thus, the necessity for personalised diagnosis is key to prevent stress-related diseases. In order to evaluate stress levels, a multi-sensing system is proposed based on non-invasive EEG and ECG signals. A target population of 24 individuals which age range between 18-23 years old are intentionally exposed to control-induced stress tests while EEG and ECG are simultaneously recorded. The acquired signals are processed by using semisupevised Machine Learning techniques as those provide a patient-specific approach due to key characteristics such as adaptiveness and robustness. In here, a stress metric is proposed that jointly with each individual medical history provide mechanisms to prevent and avoid possible chronic-health issues for individuals whom are more sensitive to stressors. Finally, supervised learning techniques are used to classify the obtained featured clusters to evaluate specific and general subject models in order to pave the way for real time stress monitoring.
Competing Interests: Declaration of Competing Interest The authors declare no competing interests.
(Copyright © 2021. Published by Elsevier B.V.)
Databáze: MEDLINE
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