Sensors 2013, 13(5), 6730-6745; doi:10.3390/s130506730
On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis
Karmele López-de-Ipiña 1,*
, Jesus-Bernardino Alonso 2, Carlos Manuel Travieso 2, Jordi Solé-Casals 3
, Harkaitz Egiraun 1,4, Marcos Faundez-Zanuy 5, Aitzol Ezeiza 1, Nora Barroso 1, Miriam Ecay-Torres 6, Pablo Martinez-Lage 6 and Unai Martinez de Lizardui 1
, Harkaitz Egiraun 1,4, Marcos Faundez-Zanuy 5, Aitzol Ezeiza 1, Nora Barroso 1, Miriam Ecay-Torres 6, Pablo Martinez-Lage 6 and Unai Martinez de Lizardui 11
Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain
2
Signal and Communication Departament (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Campus of Tafira, Las Palmas de Gran Canaria 35017, Spain
3
Digital Technologies Group, University of Vic, Sagrada família 7, Vic 08500, Spain
4
Research Center for Experimental Marine Biology and Biotechnology, Plentzia Marine Station, University of the Basque Country, Plentzia 48620, Spain
5
Escola Universitaria Politècnica de Mataró (UPC), Tecnocampus, Mataró, Barcelona 08302, Spain
6
CITA-Alzheimer Foundation, San Sebastian 20009, Spain
*
Author to whom correspondence should be addressed.
Received: 15 April 2013 / Revised: 8 May 2013 / Accepted: 13 May 2013 / Published: 21 May 2013
(This article belongs to the Special Issue Select papers from UCAmI & IWAAL 2012 - the 6th International Conference on Ubiquitous Computing and Ambient Intelligence & 4th International Workshop on Ambient Assisted Living (UCAmI & IWAAL 2012))
Abstract
The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients. View Full-TextKeywords:
Alzheimer’s disease diagnosis; spontaneous speech; emotion recognition; machine learning; non-invasive diagnostic techniques; dementia
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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MDPI and ACS Style
López-de-Ipiña, K.; Alonso, J.-B.; Travieso, C.M.; Solé-Casals, J.; Egiraun, H.; Faundez-Zanuy, M.; Ezeiza, A.; Barroso, N.; Ecay-Torres, M.; Martinez-Lage, P.; Lizardui, U.M. On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis. Sensors 2013, 13, 6730-6745.