TY - JOUR A2 - Guillén, Alberto AU - Belle, Ashwin AU - Hargraves, Rosalyn Hobson AU - Najarian, Kayvan PY - 2012 DA - 2012/08/09 TI - An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram SP - 528781 VL - 2012 AB - 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. SN - 1748-670X UR - https://doi.org/10.1155/2012/528781 DO - 10.1155/2012/528781 JF - Computational and Mathematical Methods in Medicine PB - Hindawi Publishing Corporation KW - ER -