Conference proceeding
Combining Multichannel ERP Data for Early Diagnosis of Alzheimer's Disease
2009 4TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, p515
International IEEE EMBS Conference on Neural Engineering
01 Jan 2009
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
As the average age of our population increases, the prevalence of Alzheimer's Disease (AD), the most common form of dementia, has grown sharply. Current diagnosis of AD primarlly uses longitudinal clinical evaluations and/or invasive lumbar punctures for CSF analysis, available only at specialized hospitals, which are generally outside of financial and geographical reach of most patients. We expand on our previous work and describe an ensemble of classifiers based approach that combines decision and data fusion techniques for the early diagnosis of AD using event related potentials (ERP) obtained in response to different audio stimuli. In this contribution, we specifically examine various feature set combinations, obtained from different EEG electrode locations and in response to different stimulus tones to illustrate the accuracy of such H system for AD diagnosis at the earliest stage on a clinically significant cohort size of 71 patients.
Metrics
3 Record Views
Details
- Title
- Combining Multichannel ERP Data for Early Diagnosis of Alzheimer's Disease
- Creators
- Metin Ahiskali - Rowan Univ, Glassboro, NJ 08028 USARobi Polikar - Rowan Univ, Glassboro, NJ 08028 USAJohn Kounios - Drexel UniversityDeborah Green - Drexel Univ, Dept Psychol, Philadelphia, PA USAChristopher M. Clark - Univ Pennsylvania, Dept Neurol, Philadelphia, PA USAIEEE
- Publication Details
- 2009 4TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, p515
- Series
- International IEEE EMBS Conference on Neural Engineering
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- P30 AG10124- R01 AG022272 / Neuronetrix ECS-0239090 / National Science Foundation; National Science Foundation (NSF) SAP4100027296 / PA Dept. of Health
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Psychology
- Identifiers
- 991019170385804721
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Biomedical
- Neurosciences