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Authors: F. Riganello 1 and A. Candelieri 2

Affiliations: 1 S. Anna Institute and RAN – Research on Advanced Neuro-rehabilitation, Italy ; 2 University of Calabria, Italy

Keyword(s): Heart Rate Variability Analysis, Data Mining, Music, Traumatic Brain Injury, Vegetative State.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cloud Computing ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; e-Health ; Enterprise Information Systems ; Health Information Systems ; Platforms and Applications ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Aims of the study are to 1-classify emotional responses in healthy and conscious brain injured subjects by Data Mining analysis of subjective reports and Heart Rate Variability (HRV), 2-compare different procedures for reliability, and 3-test applicability in patients with disordered consciousness (vegetative state). We measured HRV of 26 healthy and 16 posttraumatic subjects listening music samples selected by emotions they evoke. Each subject was interviewed and the reported emotions were used for identifing a model assessing the most probable emotion by the HRV parameters. Two macro-categories were defined: positive and negative emotions. The study matched a three-phases strategy. First, we applied several classification approaches to healthy subjects evaluating them through suitable validation techniques. Secondly, the best performing classifiers were used to forecast emotions of posttraumatic patients, without retraining. In the 3rd phase we used the most reliable decision model both for validation (1st phase) and independent test (2nd phase) in order to classify the “emotional” response of 9 subjects in vegetative state. One HRV parameter (normalized Low-Frequency Band Power) proved sufficient to forecast a reliable classification. Accuracy was greater than 70% on training, validation and test. Model represents an objective criterion to investigate possible emotional responses also in unconscious patients. (More)

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Paper citation in several formats:
Riganello, F. and Candelieri, A. (2010). DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND EMOTIONAL PROCESSING - Comparative Analyses, Validation and Application. In Proceedings of the Third International Conference on Health Informatics (BIOSTEC 2010) - HEALTHINF; ISBN 978-989-674-016-0; ISSN 2184-4305, SciTePress, pages 159-165. DOI: 10.5220/0002691101590165

@conference{healthinf10,
author={F. Riganello. and A. Candelieri.},
title={DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND EMOTIONAL PROCESSING - Comparative Analyses, Validation and Application},
booktitle={Proceedings of the Third International Conference on Health Informatics (BIOSTEC 2010) - HEALTHINF},
year={2010},
pages={159-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002691101590165},
isbn={978-989-674-016-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the Third International Conference on Health Informatics (BIOSTEC 2010) - HEALTHINF
TI - DATA MINING AND THE FUNCTIONAL RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND EMOTIONAL PROCESSING - Comparative Analyses, Validation and Application
SN - 978-989-674-016-0
IS - 2184-4305
AU - Riganello, F.
AU - Candelieri, A.
PY - 2010
SP - 159
EP - 165
DO - 10.5220/0002691101590165
PB - SciTePress