A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings

Cosimo Ieracitano, Nadia Mammone, Alessia Bramanti, Amir Hussain, Francesco C. Morabito

Research output: Contribution to journalArticle

Abstract

A data-driven machine deep learning approach is proposed for differentiating subjects with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC), by only analyzing noninvasive scalp EEG recordings. The methodology here proposed consists of evaluating the power spectral density (PSD) of the 19-channels EEG traces and representing the related spectral profiles into 2-d gray scale images (PSD-images). A customized Convolutional Neural Network with one processing module of convolution, Rectified Linear Units (ReLu) and pooling layer (CNN1) is designed to extract from PSD-images some suitable features and to perform the corresponding two and three-ways classification tasks. The resulting CNN is shown to provide better classification performance when compared to more conventional learning machines; indeed, it achieves an average accuracy of 89.8% in binary classification and of 83.3% in three-ways classification. These results encourage the use of deep processing systems (here, an engineered first stage, namely the PSD-image extraction, and a second or multiple CNN stage) in challenging clinical frameworks.

Original languageEnglish
Pages (from-to)96-107
Number of pages12
JournalNeurocomputing
Volume323
DOIs
Publication statusPublished - Jan 5 2019

Keywords

  • Alzheimer's disease
  • Convolutional Neural Network
  • Deep learning
  • Mild Cognitive Impairment
  • Power spectral density

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings'. Together they form a unique fingerprint.

  • Cite this