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Authors: Liliana A. S. Medina and Ana L. N. Fred

Affiliation: Instituto Superior Técnico, Portugal

ISBN: 978-989-674-021-4

Keyword(s): Genetic algorithm, Unsupervised learning, Temporal data, Electrocardiogram, Stress detection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Electrocardiography signals are typically analyzed for medical diagnosis of pathologies and are relatively unexplored as physiological behavioral manifestations. In this work we propose to analyze these signals with the intent of assessing the existence of significant changes of their features related to stress occurring in the performance of a computer-based cognitive task. Given the exploratory nature of this analysis, usage of unsupervised learning techniques is naturally adequate for our purposes. We propose a work methodology based on unsupervised automatic methods, namely clustering algorithms and clustering ensemble methods, as well as on evolutionary algorithms. The implemented automatic methods are the result of the adaptation of existing clustering techniques, including evolutionary computation, with the goal of detecting patterns by analysis of data with continuous temporal evolution. We propose a genetic algorithm for the specific task of assessing the continuous evoluti on and the separability of the stress states. The obtained results show the existence of differentiated states in the data sets that represent the ECG signals, thus confirming the adequacy and validity of the proposed methodology in the context of the exploration of these electrophysiological signals for emotional states detection. (More)

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Paper citation in several formats:
A. S. Medina L.; L. N. Fred A. and (2010). GENETIC ALGORITHM FOR CLUSTERING TEMPORAL DATA - Application to the Detection of Stress from ECG Signals.In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 135-142. DOI: 10.5220/0002752601350142

@conference{icaart10,
author={Liliana {A. S. Medina} and Ana {L. N. Fred}},
title={GENETIC ALGORITHM FOR CLUSTERING TEMPORAL DATA - Application to the Detection of Stress from ECG Signals},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={135-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002752601350142},
isbn={978-989-674-021-4},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - GENETIC ALGORITHM FOR CLUSTERING TEMPORAL DATA - Application to the Detection of Stress from ECG Signals
SN - 978-989-674-021-4
AU - A. S. Medina, L.
AU - L. N. Fred, A.
PY - 2010
SP - 135
EP - 142
DO - 10.5220/0002752601350142

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