Cognitive Computation

, Volume 7, Issue 1, pp 44–55

On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

  • K. López-de-Ipiña
  • J. B. Alonso
  • J. Solé-Casals
  • N. Barroso
  • P. Henriquez
  • M. Faundez-Zanuy
  • C. M. Travieso
  • M. Ecay-Torres
  • P. Martínez-Lage
  • H. Eguiraun
Article

DOI: 10.1007/s12559-013-9229-9

Cite this article as:
López-de-Ipiña, K., Alonso, J.B., Solé-Casals, J. et al. Cogn Comput (2015) 7: 44. doi:10.1007/s12559-013-9229-9

Abstract

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore, it is one of the most active research areas today. Alzheimer’s disease is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a postmortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early AD detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of AD by noninvasive methods. The purpose is to examine, in a pilot study, the potential of applying machine learning algorithms to speech features obtained from suspected Alzheimer’s disease sufferers in order to help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: spontaneous speech and emotional response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of AD patients.

Keywords

Alzheimer’s disease diagnosis Spontaneous speech Emotion recognition 

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • K. López-de-Ipiña
    • 1
  • J. B. Alonso
    • 2
  • J. Solé-Casals
    • 3
  • N. Barroso
    • 1
  • P. Henriquez
    • 2
  • M. Faundez-Zanuy
    • 4
  • C. M. Travieso
    • 2
  • M. Ecay-Torres
    • 5
  • P. Martínez-Lage
    • 5
  • H. Eguiraun
    • 1
    • 6
  1. 1.Systems Engineering and Automation DepartmentUniversity of the Basque CountryDonostiaSpain
  2. 2.IDeTICUniversidad de Las Palmas de Gran CanariaLas PalmasSpain
  3. 3.Digital Technologies Group University of VicCataloniaSpain
  4. 4.Escola Universitària Politècnica de Mataró (UPC)BarcelonaSpain
  5. 5.CITA-Alzheimer FoundationDonostiaSpain
  6. 6.Research Center for Experimental Marine Biology and Biotechnology, Plentzia Marine StationUniversity of the Basque CountryPlentziaSpain

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