Clustering and modeling of EEG coherence features of Alzheimer's and mild cognitive impairment patients

• Published in: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2008; pp. 1092 - 1095
• Main Authors: Akrofi, Kwaku, Baker, Mary C., O'Boyle, Michael W., Schiffer, Randolph B.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2008
• Subjects: Aged; Alzheimer Disease - complications; Alzheimer Disease - diagnosis; Alzheimer Disease - physiopathology; Artificial Intelligence; Cluster Analysis; Cognition Disorders - diagnosis; Cognition Disorders - etiology; Cognition Disorders - physiopathology; Computer Simulation; Diagnosis, Computer-Assisted - methods; Electroencephalography - methods; Female; Humans; Male; Models, Neurological; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity
• ISBN: 9781424418145; 1424418143
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2008.4649350

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 controls. With MDA, an overall classification rate (CR) of 84% was obtained for AD vs. MCI vs. Controls classifications. The high CR implies that it is possible to distinguish between the three groups. The coherence features were also statistically analyzed to derive a neural model of AD and MCI, which indicated that patients with AD may have a greater number of damaged cortical fibers than their MCI counterparts, and furthermore, that MCI may be an intermediary step in the development of AD.