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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 528781, 12 pages
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.
- J. Locke, An Essay Concerning Human Understanding, TE Zell, 1847.
- E. Horvitz and J. Apacible, “Learning and reasoning about interruption,” in Proceedings of the 5th International Conference on Multimodal Interfaces (ICMI '03), pp. 20–27, November 2003.
- D. S. McCrickard and C. M. Chewar, “Attuning notification design to user goals and attention costs,” Communications of the ACM, vol. 46, no. 3, pp. 67–72, 2003.
- C. Roda, A. Angehrn, T. Nabeth, and L. Razmerita, “Using conversational agents to support the adoption of knowledge sharing practices,” Interacting with Computers, vol. 15, no. 1, pp. 57–89, 2003.
- B. P. Bailey, P. D. Adamczyk, T. Y. Chang, and N. A. Chilson, “A framework for specifying and monitoring user tasks,” Computers in Human Behavior, vol. 22, no. 4, pp. 709–732, 2006.
- S. K. L. Lal and A. Craig, “A critical review of the psychophysiology of driver fatigue,” Biological Psychology, vol. 55, no. 3, pp. 173–194, 2001.
- F. Mamashli, M. Ahmadlu, M. R. H. Golpayegani, and S. Gharibzadeh, “Detection of attention using chaotic global features,” Journal of Neuropsychiatry and Clinical Neurosciences, vol. 22, no. 2, article E20, 2010.
- S. K. L. Lal and A. Craig, “Driver fatigue: electroencephalography and psychological assessment,” Psychophysiology, vol. 39, no. 3, pp. 313–321, 2002.
- A. R. Clarke, R. J. Barry, R. McCarthy, and M. Selikowitz, “EEG analysis in attention-deficit/hyperactivity disorder: a comparative study of two subtypes,” Psychiatry Research, vol. 81, no. 1, pp. 19–29, 1998.
- J. F. Lubar, “Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders,” Biofeedback and Self-Regulation, vol. 16, no. 3, pp. 201–225, 1991.
- T. P. Tinius and K. A. Tinius, “Changes after EEG biofeedback and cognitive retraining in adults with mild traumatic brain injury and attention deficit hyperactivity disorder,” Journal of Neurotherapy, vol. 4, pp. 27–44, 2000.
- A. Al-Ahmad, M. Homer, and P. Wang, Accuracy and Utility of Multi-Sensor Armband ECG Signal Compared to Holder Monitoring, Arrhythmia Technologies Retreat, Chicago, Ill, USA, 2004.
- J. M. Stern and J. Engel, Atlas of EEG Patterns, Lippincott Williams & Wilkins, 2004.
- S. J. Orfanidis, Introduction to Signal Processing, Prentice-Hall, 1995.
- G. M. Friesen, T. C. Jannett, M. Afify Jadallah, S. L. Yates, S. R. Qu int, and H. Troy Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 1, pp. 85–98, 1990.
- J. B. Ochoa, Eeg Signal Classification for Brain Computer Interface Applications, Ecole Polytechnique Federale De Lausanne, 2002.
- M. R. Portnoff, “Time-frequency representation of digital signals and systems based on short-time Fourier analysis,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 1, pp. 55–59, 1980.
- D. Gabor, “Theory of communication. Part 1: the analysis of information,” Electrical Engineers-Part III, vol. 93, pp. 429–441, 1946.
- P. Bloomfield, Fourier Analysis of Time Series: An Introduction, Wiley-Interscience, 2004.
- L. Cohen, “Time-frequency distributions—a review,” Proceedings of the IEEE, vol. 77, no. 7, pp. 941–981, 1989.
- Y. Zhao, L. E. Atlas, and R. J. Marks, “Use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, no. 7, pp. 1084–1091, 1990.
- H. I. Choi and W. J. Williams, “Improved time-frequency representation of multicomponent signals using exponential kernels,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 6, pp. 862–871, 1989.
- F. Hlawatsch and G. F. Boudreaux-Bartels, “Linear and quadratic time-frequency signal representations,” IEEE Signal Processing Magazine, vol. 9, no. 2, pp. 21–67, 1992.
- A. Belle, R. Hobson, and K. Najarian, “A physiological signal processing system for optimal engagement and attention detection,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '11), pp. 555–561, 2011.
- A. Belle, S. Y. Ji, S. Ansari, R. Hakimzadeh, K. Ward, and K. Najarian, “Frustration detection with electrocardiograph signal using wavelet transform,” in Proceedings of the 1st International Conference on Biosciences (BioSciencesWorld '10), pp. 91–94, March 2010.
- R. G. Stockwell, L. Mansinha, and R. P. Lowe, “Localization of the complex spectrum: the S transform,” IEEE Transactions on Signal Processing, vol. 44, no. 4, pp. 998–1001, 1996.
- A. Babloyantz, J. M. Salazar, and C. Nicolis, “Evidence of chaotic dynamics of brain activity during the sleep cycle,” Physics Letters A, vol. 111, no. 3, pp. 152–156, 1985.
- K. Natarajan, U. R. Acharya, F. Alias, T. Tiboleng, and S. K. Puthusserypady, “Nonlinear analysis of EEG signals at different mental states,” BioMedical Engineering Online, vol. 3, article 7, 2004.
- E. Başar, C. Başar-Eroglu, S. Karakaş, and M. Schürmann, “Brain oscillations in perception and memory,” International Journal of Psychophysiology, vol. 35, no. 2-3, pp. 95–124, 2000.
- V. J. Samar, A. Bopardikar, R. Rao, and K. Swartz, “Wavelet analysis of neuroelectric waveforms: a conceptual tutorial,” Brain and Language, vol. 66, no. 1, pp. 7–60, 1999.
- E. A. Bartnik, K. J. Blinowska, and P. J. Durka, “Single evoked potential reconstruction by means of wavelet transform,” Biological Cybernetics, vol. 67, no. 2, pp. 175–181, 1992.
- O. Bertrand, J. Bohorquez, and J. Pernier, “Time-frequency digital filtering based on an invertible wavelet transform: an application to evoked potentials,” IEEE Transactions on Biomedical Engineering, vol. 41, no. 1, pp. 77–88, 1994.
- L. J. Trejo and M. J. Shensa, “Feature extraction of event-related potentials using wavelets: an application to human performance monitoring,” Brain and Language, vol. 66, no. 1, pp. 89–107, 1999.
- T. Kalayci, O. Ozdamar, and N. Erdol, “Use of wavelet transform as a preprocessor for the neural network detection of EEG spikes,” in Proceedings of the IEEE Creative Technology Transfer-A Global Affair (Southeastcon '94), pp. 1–3, April 1994.
- D. M. Tucker, “Spatial sampling of head electrical fields: the geodesic sensor net,” Electroencephalography and Clinical Neurophysiology, vol. 87, no. 3, pp. 154–163, 1993.
- S. J. Schiff, J. G. Milton, J. Heller, and S. L. Weinstein, “Wavelet transforms and surrogate data for electroencephalographic spike and seizure localization,” Optical Engineering, vol. 33, no. 7, pp. 2162–2169, 1994.
- J. Raz, L. Dickerson, and B. Turetsky, “A wavelet packet model of evoked potentials,” Brain and Language, vol. 66, no. 1, pp. 61–88, 1999.
- E. Frank, Y. Wang, S. Inglis, G. Holmes, and I. H. Witten, “Using model trees for classification,” Machine Learning, vol. 32, no. 1, pp. 63–76, 1998.
- J. R. Quinlan, C4. 5: Programs for Machine Learning, Morgan kaufmann, 1993.
- J. R. Quinlan, “Bagging, boosting, and C4.5,” in Proceedings of the 13th National Conference on Artificial Intelligence (AAAI '96), pp. 725–730, August 1996.
- J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986.
- C. A. Frantzidis, C. Bratsas, M. A. Klados et al., “On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, pp. 309–318, 2010.
- C. D. Katsis, N. S. Katertsidis, and D. I. Fotiadis, “An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders,” Biomedical Signal Processing and Control, vol. 6, no. 3, pp. 261–268, 2011.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
- A. Liaw and M. Wiener, “Classification and regression by random forest,” R News, vol. 2, pp. 18–22, 2002.
- J. Krajewski, S. Schnieder, D. Sommer, A. Batliner, and B. Schuller, “Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech,” Neurocomputing, vol. 84, pp. 65–75, 2012.
- S. Y. Ji, R. Smith, T. Huynh, and K. Najarian, “A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries,” BMC Medical Informatics and Decision Making, vol. 9, no. 1, article 2, 2009.
- K. Najarian, “Learning-based complexity evaluation of radial basis function networks,” Neural Processing Letters, vol. 16, no. 2, pp. 137–150, 2002.
- K. Najarian, G. A. Dumont, M. S. Davies, and N. Heckman, “PAC learning in non‐linear FIR models,” International Journal of Adaptive Control and Signal Processing, vol. 15, pp. 37–52, 2001.
- K. Najarian, “A fixed-distribution PAC learning theory for neural FIR models,” Journal of Intelligent Information Systems, vol. 25, no. 3, pp. 275–291, 2005.