GENETIC ALGORITHM FOR CLUSTERING TEMPORAL DATA - Application to the Detection of Stress from ECG Signals

Liliana A. S. Medina, Ana L. N. Fred

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 evolution 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.

References

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Paper Citation


in Harvard Style

A. S. Medina L. and L. N. Fred A. (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


in Bibtex Style

@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},
}


in EndNote Style

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