Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia

Francesco Carlo Morabito, Maurizio Campolo, Nadia Mammone, Mario Versaci, Silvana Franceschetti, Fabrizio Tagliavini, Vito Sofia, Daniela Fatuzzo, Antonio Gambardella, Angelo Labate, Laura Mumoli, Giovanbattista Gaspare Tripodi, Sara Gasparini, Vittoria Cianci, Chiara Sueri, Edoardo Ferlazzo, Umberto Aguglia

Research output: Contribution to journalArticle

Abstract

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Original languageEnglish
Pages (from-to)1650039
JournalInternational Journal of Neural Systems
Volume27
Issue number2
DOIs
Publication statusE-pub ahead of print - May 3 2016

Keywords

  • Aged
  • Alzheimer Disease
  • Brain
  • Creutzfeldt-Jakob Syndrome
  • Diagnosis, Differential
  • Disease Progression
  • Electroencephalography
  • Female
  • Humans
  • Male
  • Middle Aged
  • Retrospective Studies
  • Sensitivity and Specificity
  • Support Vector Machine
  • Wavelet Analysis
  • Journal Article
  • Multicenter Study

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  • Cite this

    Morabito, F. C., Campolo, M., Mammone, N., Versaci, M., Franceschetti, S., Tagliavini, F., Sofia, V., Fatuzzo, D., Gambardella, A., Labate, A., Mumoli, L., Tripodi, G. G., Gasparini, S., Cianci, V., Sueri, C., Ferlazzo, E., & Aguglia, U. (2016). Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia. International Journal of Neural Systems, 27(2), 1650039. https://doi.org/10.1142/S0129065716500398