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Prediction of state anxiety by machine learning applied to photoplethysmography data
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). Amon...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812926/ https://www.ncbi.nlm.nih.gov/pubmed/33520434 http://dx.doi.org/10.7717/peerj.10448 |
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author | Perpetuini, David Chiarelli, Antonio Maria Cardone, Daniela Filippini, Chiara Rinella, Sergio Massimino, Simona Bianco, Francesco Bucciarelli, Valentina Vinciguerra, Vincenzo Fallica, Piero Perciavalle, Vincenzo Gallina, Sabina Conoci, Sabrina Merla, Arcangelo |
author_facet | Perpetuini, David Chiarelli, Antonio Maria Cardone, Daniela Filippini, Chiara Rinella, Sergio Massimino, Simona Bianco, Francesco Bucciarelli, Valentina Vinciguerra, Vincenzo Fallica, Piero Perciavalle, Vincenzo Gallina, Sabina Conoci, Sabrina Merla, Arcangelo |
author_sort | Perpetuini, David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7812926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78129262021-01-28 Prediction of state anxiety by machine learning applied to photoplethysmography data Perpetuini, David Chiarelli, Antonio Maria Cardone, Daniela Filippini, Chiara Rinella, Sergio Massimino, Simona Bianco, Francesco Bucciarelli, Valentina Vinciguerra, Vincenzo Fallica, Piero Perciavalle, Vincenzo Gallina, Sabina Conoci, Sabrina Merla, Arcangelo PeerJ Bioengineering 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. PeerJ Inc. 2021-01-15 /pmc/articles/PMC7812926/ /pubmed/33520434 http://dx.doi.org/10.7717/peerj.10448 Text en © 2021 Perpetuini et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioengineering Perpetuini, David Chiarelli, Antonio Maria Cardone, Daniela Filippini, Chiara Rinella, Sergio Massimino, Simona Bianco, Francesco Bucciarelli, Valentina Vinciguerra, Vincenzo Fallica, Piero Perciavalle, Vincenzo Gallina, Sabina Conoci, Sabrina Merla, Arcangelo Prediction of state anxiety by machine learning applied to photoplethysmography data |
title | Prediction of state anxiety by machine learning applied to photoplethysmography data |
title_full | Prediction of state anxiety by machine learning applied to photoplethysmography data |
title_fullStr | Prediction of state anxiety by machine learning applied to photoplethysmography data |
title_full_unstemmed | Prediction of state anxiety by machine learning applied to photoplethysmography data |
title_short | Prediction of state anxiety by machine learning applied to photoplethysmography data |
title_sort | prediction of state anxiety by machine learning applied to photoplethysmography data |
topic | Bioengineering |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812926/ https://www.ncbi.nlm.nih.gov/pubmed/33520434 http://dx.doi.org/10.7717/peerj.10448 |
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