Open AccessPrediction of state anxiety by machine learning applied to photoplethysmography data
Author(s) -
David Perpetuini,
Antonio Maria Chiarelli,
Daniela Cardone,
Chiara Filippini,
Sergio Rinella,
Simona Massimino,
Francesco Bianco,
Valentina Bucciarelli,
Vincenzo Vinciguerra,
P. G. Fallica,
Vincenzo Perciavalle,
Sabina Gallina,
Sabrina Conoci,
Arcangelo Merla
Publication year - 2021
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.10448
Subject(s) - photoplethysmogram , anxiety , artificial intelligence , computer science , machine learning , support vector machine , correlation , pattern recognition (psychology) , cognition , psychophysiology , speech recognition , psychology , mathematics , filter (signal processing) , neuroscience , geometry , psychiatry , computer vision
Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm ( r = 0.81; p = 1.87∙10 −9 ). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.