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Your search keyword '"Unai Martinez de Lizardui"' showing total 6 results
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6 results on '"Unai Martinez de Lizardui"'

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

Author

Unai Martinez de Lizardui, Nora Barroso, Miriam Ecay-Torres, Pablo Martinez-Lage, Marcos Faundez-Zanuy, Aitzol Ezeiza, Jordi Solé-Casals, Harkaitz Egiraun, Carlos Manuel Travieso, Jesus-Bernardino Alonso, and Karmele López-de-Ipiña

Subjects
Alzheimer’s disease diagnosis, spontaneous speech, emotion recognition, machine learning, non-invasive diagnostic techniques, dementia, Chemical technology, TP1-1185
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.

Published
2013
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6. On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

Author

Jesus-Bernardino Alonso, Marcos Faundez-Zanuy, Aitzol Ezeiza, Jordi Solé-Casals, Carlos M. Travieso, Karmele López-de-Ipiña, Nora Barroso, Miriam Ecay-Torres, Pablo Martinez-Lage, Unai Martinez de Lizardui, Harkaitz Egiraun, Universitat de Vic. Escola Politècnica Superior, and Universitat de Vic. Grup de Recerca en Tecnologies Digitals

Subjects
Male, BIOCHEMISTRY AND MOLECULAR BIOLOGY, Computer science, Emotions, Pilot Projects, 02 engineering and technology, spontaneous speech, computer.software_genre, lcsh:Chemical technology, Biochemistry, Analytical Chemistry, Automation, 0302 clinical medicine, emotion recognition, 0202 electrical engineering, electronic engineering, information engineering, Alzheimer's disease diagnosis, lcsh:TP1-1185, ELECTRICAL AND ELECTRONIC ENGINEERING, Instrumentation, Diagnostic Techniques and Procedures, Aged, 80 and over, Artificial neural network, Temperature, Signal Processing, Computer-Assisted, Middle Aged, Atomic and Molecular Physics, and Optics, Fractals, machine learning, 020201 artificial intelligence & image processing, Female, Alzheimer's disease, Adult, Feature selection, Machine learning, Article, Alzheimer’s disease diagnosis, non-invasive diagnostic techniques, dementia, CHEMISTRY, ANALYTICAL, 03 medical and health sciences, Young Adult, Alzheimer Disease, medicine, Dementia, Humans, Speech, Selection (genetic algorithm), Aged, business.industry, Non invasive, medicine.disease, Alzheimer, Malaltia d', Processament de la parla, Artificial intelligence, business, PHYSICS, ATOMIC, MOLECULAR AND CHEMICAL, computer, 030217 neurology & neurosurgery
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. his work has been partially supported by a SAIOTEK from the Basque Government, University of Vic under the research grant R0904, and the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03. Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country & IKERBASQUE, Basque Foundation for Science

Published
2013

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