Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 1; pp. 527 - 536
• Main Authors: Mammone, Nadia, De Salvo, Simona, Bonanno, Lilla, Ieracitano, Cosimo, Marino, Silvia, Marra, Angela, Bramanti, Alessia, Morabito, Francesco C.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alzheimer's disease; Alzheimer's disease (AD); Brain; brain connectivity; Clustering; Complex networks; Compression tests; compressive sensing (CS); decision support; Dementia; Density; Electrodes; Electroencephalography; healthcare signal processing; high-density EEG (HD-EEG); mild cognitive impairment; Network analysis; Organizations; permutation disalignment index; Permutations; Standards organizations
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2018.2868431

Alzheimer's disease (AD) is a neurodegenerative disorder that causes a loss of connections between neurons. The goal of this paper is to construct a complex network model of the brain-electrical activity, using high-density EEG (HD-EEG) recordings, and to compare the network organization in AD, mild cognitive impaired (MCI), and healthy control (CNT) subjects. The HD-EEG of 16 AD, 16 MCI, and 12 CNT was recorded during an eye-closed resting state. The permutation disalignment index (PDI) was used to describe the dissimilarity between EEG signals and to construct the connection matrices of the network model. The three groups were found to have significantly different ( p <inline-formula><tex-math notation="LaTeX"> <</tex-math></inline-formula> 0.001) characteristic path length (<inline-formula><tex-math notation="LaTeX">\lambda</tex-math></inline-formula>), average clustering coefficient (CC), and the global efficiency (GE). This is the first time that HD-EEG signals of AD, MCI, and CNT have been compared and that PDI has been used to discriminate between the three groups. Considering the large amount of data originating from HD-EEG acquisition, compared to standard EEG, the aim of this paper is also to assess that compression did not alter the results of the complex network analysis. Compressive sensing was adopted to compress and reconstruct the HD-EEG signals with minimal information loss, achieving an average structural similarity index of 0.954 (AD), 0.957 (MCI), and 0.959 (CNT). When applied to the reconstructed HD-EEG, complex network analysis provided a substantially unaltered performance, compared to the analysis of the original signals: <inline-formula><tex-math notation="LaTeX">\lambda</tex-math></inline-formula>, CC, and GE of the three groups were indeed still significantly different ( p <inline-formula><tex-math notation="LaTeX"> <</tex-math></inline-formula> 0.001).