_:n62a0251cce17434fab2843caf5fa4858xb41
<http://schema.org/description>
"Automatic identification and categorization of Alzheimer's patients and the\nability to distinguish between different levels of this disease can be very\nhelpful to the research community in this field, since other non-automatic\napproaches are very time-consuming and are highly dependent on experts'\nexperience. Herein, we propose the utility of cerebral instantaneous phase and\nenvelope information in order to discriminate between Alzheimer's patients, MCI\nsubjects and healthy normal individuals from functional magnetic resonance\nimaging (fMRI) data. To this end, after performing the region-of-interest (ROI)\nanalysis on fMRI data, different features covering power, entropy and coherency\naspects of data are derived from instantaneous phase and envelope sequences of\nROI signals. Various sets of features are calculated and fed to a sequential\nforward floating feature selection (SFFFS) to choose the most discriminative\nand informative sets of features. A Student's t-test has been used to select\nthe most relevant features from chosen sets. Finally, a K-NN classifier is used\nto distinguish between classes in a three-class categorization problem. The\nreported performance in overall accuracy using fMRI data of 111 combined\nsubjects, is 80.1\ncategories distinction and is comparable to the state-of-the-art approaches\nrecently proposed in this regard. The significance of obtained results was\nstatistically confirmed by evaluating through standard classification\nperformance indicators. The obtained results illustrate that introduced\nanalytic phase and envelope feature indexes derived from the ROI signals are\nsignificantly discriminative in distinguishing between Alzheimer's patients and\nNormal healthy subject."
<https://deepai.org/profile/esmaeil-seraj>