Paper Info
Title
Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings
Abstract
In spite of the worldwide financial and research efforts made, the pathophysiological mechanism at the basis of Alzheimer's disease (AD) is still poorly understood. Previous studies using electroencephalography (EEG) have focused on the slowing of oscillatory brain rhythms, coupled with complexity reduction of the corresponding time-series and their enhanced compressibility. These analyses have been typically carried out on single channels. However, limited investigations have focused on the possibility yielded by computational intelligence methodologies and novel machine learning approaches applied to multichannel schemes. The study at screening level on EEG recordings of subjects at risk could be useful to highlight the emergence of underlying AD progression (or at least support any further clinical investigation). In this work, the representational power of Deep Learning on Convolutional Neural Networks (CNN) is exploited to generate suitable sets of features that are then able to classify EEG patterns of AD from a prodromal version of dementia (Mild Cognitive Impairment, MCI) and from age-matched Healthy Controls (HC). The processing system here used enforces a series of convolutional-subsampling layers in order to derive a multivariate assembly of latent, novel patterns, finally used to categorize sets of EEG from different classes of subjects. The final processor here proposed is able to reach an averaged 80% of correct classification with good performance on both sensitivity and specificity by using a Multilayered Feedforward Perceptron (MLP) of the standard type as a final block of the procedure.
Year
DOI
Venue
2016
10.1109/RTSI.2016.7740576
2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)
Keywords
Field
DocType
Alzheimer's disease,Mild Cognitive Impairment,Deep Learning,Convolutional Neural Networks,Scalp EEG
Categorization,Pattern recognition,Computational intelligence,Computer science,Convolutional neural network,Artificial intelligence,Deep learning,Cognition,Perceptron,Electroencephalography,Machine learning,Dementia
Conference
ISBN
Citations 
PageRank 
978-1-5090-1132-2
2
0.45
References 
Authors
5
9
Name
Order
Citations
PageRank
Francesco Carlo Morabito133954.83
Maurizio Campolo2404.11
Cosimo Ieracitano3112.64
Javad Mohammad Ebadi420.45
Lilla Bonanno5182.15
Alessia Bramanti69911.21
Simona De Salvo720.45
Nadia Mammone813619.69
Placido Bramanti9295.24