1. Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis
- Author
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Jordi Solé-Casals, Miriam Ecay-Torres, Pablo Martinez-Lage, Harkaitz Egiraun, Jesús B. Alonso, Carlos M. Travieso, F. Zelarain, Nora Barroso, Karmele López-de-Ipiña, Universitat de Vic. Escola Politècnica Superior, Universitat de Vic. Grup de Recerca en Tecnologies Digitals, and Mediterranean Conference on Medical and Biological Engineering and Computing (13è : 2013: Sevilla)
- Subjects
Computer science ,business.industry ,Minority class ,computer.software_genre ,medicine.disease ,Alzheimer, Malaltia d' ,ComputingMethodologies_PATTERNRECOGNITION ,Outlier ,Nonlinear speech processing ,medicine ,Processament de la parla ,Artificial intelligence ,Medical diagnosis ,Alzheimer's disease ,business ,computer ,Classifier (UML) ,Natural language processing ,Spontaneous speech - Abstract
The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.