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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 528781, 12 pages
http://dx.doi.org/10.1155/2012/528781
Research Article

An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

Department of Computer Science, School of Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843019, Richmond, VA 23284-3019, USA

Received 1 May 2012; Accepted 18 June 2012

Academic Editor: Alberto Guillén

Copyright © 2012 Ashwin Belle et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.