Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 10; pp. 5122 - 5135
• Main Authors: Mammone, Nadia, Ieracitano, Cosimo, Adeli, Hojjat, Bramanti, Alessia, Morabito, Francesco C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alzheimer's disease; Alzheimer’s Disease (AD); Brain; brain connectivity; Cluster analysis; Clustering; Cognitive ability; Coherence; Complexity theory; Connectivity; Coupling; Couplings; Dementia; Density; EEG; Electrodes; electroencephalographic (EEG); Electroencephalography; Evaluation; hierarchical clustering (HC); Information processing; Learning algorithms; Machine learning; mild cognitive impairment (MCI); network density; Neural networks; Nonlinear analysis; Optical wavelength conversion; Patients; permutation entropy (PE); permutation Jaccard distance (PJD); Permutations; Signal processing; Strength; Wavelet analysis

In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the co...