| Paper: | MLSP-P6.7 | ||
| Session: | Biomedical and Other Applications | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering | ||
| Title: | Majority Vote and Decision Template Based Ensemble Classifiers Trained on Event Related Potentials for Early Diagnosis of Alzheimer’s Disease | ||
| Authors: | Nicholas Stepenosky, Rowan Unviersity, United States; Deborah Green, John Kounios, Drexel University, United States; Christopher Clark, University of Pennsylvania, United States; Robi Polikar, Rowan Unviersity, United States | ||
| Abstract: | With the rapid increase in the population of elderly individuals affected by Alzheimer’s disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates. | ||
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