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An SFFS technique for EEG feature classification to identify sub-groups
2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)
Year: 2012 | Conference Paper |
Pattern recognition techniques can be applied to problems in medicine to aid diagnostic accuracy and uncover patterns associated with disease states that are not always obvious to the clinician. In this work, a sequential forward floating search technique (SFFS) was applied to the problem of classification of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal contro...Show More
Cluster sizes in interleaved silent steady state (ISSS) imaging
Kwaku Akrofi;Bradley P. Sutton;Cheng Ouyang;Fatima T. Husain
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2011 | Conference Paper |
The effect of increasing the number of scans in the “cluster” of an interleaved silent steady state (ISSS) fMRI imaging scheme from 1 to 2, and then to 3 was examined by a fixed-effects analysis of an auditory short-term memory task with four subjects. Compared to a cluster size of 1, a cluster of 2 scans improved sensitivity at detecting brain activity and statistical power, while a cluster of 3 ...Show More
Classification of Alzheimer's disease and mild cognitive impairment by pattern recognition of EEG power and coherence
Kwaku Akrofi;Ranadip Pal;Mary C. Baker;Brian S. Nutter;Randolph W. Schiffer
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (16)
This paper describes a methodology used to classify Alzheimer's disease (AD) and mild cognitive impairment (MCI) with high accuracy using EEG data. The sequential forward floating search (SFFS) was used to select features from relative average power for channel locations in frequency bands delta, theta, alpha, and beta, and coherence between intrahemispheric channel pairs for the same frequency ra...Show More
A Model of Alzheimer's disease and mild cognitive impairment based on EEG coherence
Kwaku Akrofi;Mary C. Baker;Michael W. O'Boyle;Randolph B. Schiffer
2009 ICME International Conference on Complex Medical Engineering
Year: 2009 | Conference Paper |
Cited by: Papers (3)
EEG coherence was used to model the integrity of neural connectivity in patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and age-matched controls. Our model was derived from the EEG data of 16 AD patients, 24 MCI patients and 16 age-matched controls. We computed the coherence of the relevant channel pairs believed to represent fiber pathways. Coherence values were compared ...Show More
Clustering and modeling of EEG coherence features of Alzheimer's and mild cognitive impairment patients
Kwaku Akrofi;Mary C. Baker;Michael W. O'Boyle;Randolph B. Schiffer
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2008 | Conference Paper |
Cited by: Papers (13)
Using multiple discriminant analysis (MDA) and k-means clustering, coherence features extracted from the EEGs of a group of 56 subjects were analyzed to assess how feasible an automated coherence-based pattern recognition system that detects Alzheimer's disease (AD) would be. Sixteen of the subjects were AD patients, 24 were mild cognitive impairment (MCI) patients while 16 were age-matched contro...Show More
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