Sensors 2013, 13(5), 6730-6745; doi:10.3390/s130506730
Article

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

1 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; in revised form: 8 May 2013 / Accepted: 13 May 2013 / Published: 21 May 2013
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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.
Keywords: Alzheimer’s disease diagnosis; spontaneous speech; emotion recognition; machine learning; non-invasive diagnostic techniques; dementia

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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.

AMA Style

López-de-Ipiña K, Alonso J-B, Travieso CM, Solé-Casals J, Egiraun H, Faundez-Zanuy M, Ezeiza A, Barroso N, Ecay-Torres M, Martinez-Lage P, Lizardui UM. On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis. Sensors. 2013; 13(5):6730-6745.

Chicago/Turabian Style

López-de-Ipiña, Karmele; Alonso, Jesus-Bernardino; Travieso, Carlos M.; Solé-Casals, Jordi; Egiraun, Harkaitz; Faundez-Zanuy, Marcos; Ezeiza, Aitzol; Barroso, Nora; Ecay-Torres, Miriam; Martinez-Lage, Pablo; Lizardui, Unai M. 2013. "On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis." Sensors 13, no. 5: 6730-6745.

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