Sistema computacional para el diagnóstico de la enfermedad de Parkinson empleando el análisis de señales de voz

Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. This disorder mainly affects older adults at a rate of about 2%, and about 89% of people diagnosed with PD also have speech disorders. This has led to the development of different research in voice processing for Parkinson's patients, which allows not only a diagnosis of the pathology but also a follow-up of its evolution. In recent years, a large number of studies have focused on the automatic detection of pathologies related to the voice, in order to make objective evaluations of the voice in a non-invasive manner. In cases where the pathology primarily affects the vibratory patterns of vocal folds such as Parkinson's, the analyses typically performed are sustained vowel pronunciations. In this article, it is proposed to use information from slow and rapid variations in voice signals, also known as modulating components, combined with an effective feature reduction approach that will be used as input to the classification system. The proposed approach achieves success rates higher than 88%, surpassing the classical approach based on cepstrales coefficients on the Mel scale (MFCC), this was achieved by extracting characteristics in voice records from a Spanish language database, consequent to this was organized into a vector of characteristics and subsequent estimation was made to the diagnosis of the pathology. The results show that the information extracted from components with slow and fast variations is highly discriminatory to support the assisted diagnosis of PD. This information can also be used as a complement to existing systems.