loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Paper Unlock

Authors: F. Riganello 1 ; V. Lagani 2 ; L Pignolo 1 and A. Candelieri 2

Affiliations: 1 S. Anna Institute, Italy ; 2 University of Calabria, Italy

Keyword(s): Data Mining, Artificial Neural Network, HRV, Emotion, Vegetative State.

Abstract: Relationship between Heart Rate Variability (HRV) and emotions subjectively reported by 26 healthy subjects during symphonic music listening have been investigated through Data Mining approaches. Most reliable decision models have been successively adopted to forecast an emotional assessment on a group of 16 Traumatic Brain Injured patients during the same type of stimulation, without algorithms retraining. The most performing decisional models have been a Rule Learner (ONE-R) and a Multi Layer Perceptron (MLP) but, comparing them, the first one was the best in terms of reliability both on validation and independent test phases. Furthermore, ONE-R provides a simple “human-understandable” rule useful to evaluate emotional status of a subjects depending only on one HRV parameter: the normalized unit of Low Frequancy BandPower (nu_LF). Specifically, the classification by HRV nu_LF matched that on reported emotions, with 76.0% of correct classification; tenfold cross-validation: 70.2%; l eave-one-out validation: 71.1%. On the other hand, MLP approache has provided an accuracy of 82.69% on healthy controls, but it has decreased to 47.11% and 46.15% on 10folds-cross and leave-one-out validation respectively. Finally, the accuracy has resulted in 51.56% when the MLP model has been applied to the posttraumatic subjects, while the ONE-R accuracy has resulted in 70.31%. Data mining proved applicable in psychophysiological human research. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.173.214.227

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Riganello, F.; Lagani, V.; Pignolo, L. and Candelieri, A. (2009). Data-mining Approaches for the Study of Emotional Responses in Healthy Controls and Traumatic Brain Injured Patients: Comparative Analysis and Validation. In Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Workshop ANNIIP, (ICINCO 2009) ISBN 978-989-674-002-3, pages 125-133. DOI: 10.5220/0002263901250133

@conference{workshop anniip09,
author={F. Riganello. and V. Lagani. and L Pignolo. and A. Candelieri.},
title={Data-mining Approaches for the Study of Emotional Responses in Healthy Controls and Traumatic Brain Injured Patients: Comparative Analysis and Validation},
booktitle={Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Workshop ANNIIP, (ICINCO 2009)},
year={2009},
pages={125-133},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002263901250133},
isbn={978-989-674-002-3},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Workshop ANNIIP, (ICINCO 2009)
TI - Data-mining Approaches for the Study of Emotional Responses in Healthy Controls and Traumatic Brain Injured Patients: Comparative Analysis and Validation
SN - 978-989-674-002-3
AU - Riganello, F.
AU - Lagani, V.
AU - Pignolo, L.
AU - Candelieri, A.
PY - 2009
SP - 125
EP - 133
DO - 10.5220/0002263901250133

123movies