A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG)

• New methodology to identify MCI patients during a working memory task using MEG.
• The complete ensemble empirical mode decomposition is used to decompose the MEG.
• A nonlinear dynamics measure based on permutation entropy is used to detect features.
• Enhanced probabilistic neural network is used to distinguish MCI from healthy patients.
• It is validated using the MEG data obtained from 18 MCI and 19 control patients.

Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, greater than expected by age. A new methodology is presented to identify MCI patients during a working memory task using MEG signals. The methodology consists of four steps: In step 1, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose the MEG signal into a set of adaptive sub-bands according to its contained frequency information. In step 2, a nonlinear dynamics measure based on permutation entropy (PE) analysis is employed to analyze the sub-bands and detect features to be used for MCI detection. In step 3, an analysis of variation (ANOVA) is used for feature selection. In step 4, the enhanced probabilistic neural network (EPNN) classifier is applied to the selected features to distinguish between MCI and healthy patients. The usefulness and effectiveness of the proposed methodology are validated using the sensed MEG data obtained experimentally from 18 MCI and 19 control patients.