fMRI Based Cerebral Instantaneous Parameters for Automatic Alzheimer's, Mild Cognitive Impairment and Healthy Subject Classification
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Automatic identification and categorization of Alzheimer's patients and the ability to distinguish between different levels of this disease can be very helpful to the research community in this field, since other non-automatic approaches are very time-consuming and are highly dependent on experts' experience. Herein, we propose the utility of cerebral instantaneous phase and envelope information in order to discriminate between Alzheimer's patients, MCI subjects and healthy normal individuals from functional magnetic resonance imaging (fMRI) data. To this end, after performing the region-of-interest (ROI) analysis on fMRI data, different features covering power, entropy and coherency aspects of data are derived from instantaneous phase and envelope sequences of ROI signals. Various sets of features are calculated and fed to a sequential forward floating feature selection (SFFFS) to choose the most discriminative and informative sets of features. A Student's t-test has been used to select the most relevant features from chosen sets. Finally, a K-NN classifier is used to distinguish between classes in a three-class categorization problem. The reported performance in overall accuracy using fMRI data of 111 combined subjects, is 80.1 categories distinction and is comparable to the state-of-the-art approaches recently proposed in this regard. The significance of obtained results was statistically confirmed by evaluating through standard classification performance indicators. The obtained results illustrate that introduced analytic phase and envelope feature indexes derived from the ROI signals are significantly discriminative in distinguishing between Alzheimer's patients and Normal healthy subject.
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