KSG estimation of reconstruction delay to detect vocal disorders in nonlinear dynamical analysis

Abstract

This research investigates the applicability of a relatively new estimator of mutual information, KSG estimator, to find the reconstruction delay of phase space in dynamical systems. There are evidences that the KSG estimator is more accurate than the naive method commonly used. Methods: In this paper we estimated mutual information between the voice signals and their delayed versions, with KSG method. The voice signals were obtained from a disordered voice database. Then, we found the reconstruction delay where mutual information reached its first minimum. We applied the encountered value of reconstruction delay in linear discriminant analysis, in order to discriminate between healthy and pathological voices or to discriminate between pathologies. Discrimination between voice pathologies using nonlinear measurements is still not much explored. Moreover, in this paper we used a single nonlinear measurement: reconstruction delay. Results: The results show that the reconstruction delay obtained with KSG method has increased classification rates in most cases, in terms of accuracy, sensitivity and specificity, when compared to the naive estimator usually adopted. Conclusion: The KSG estimator is a promising technique to improve the diagnosis of voice related pathologies.

Índice de Não Estacionariedade aplicado à Classificação de Desordens Vocais

“…Quando comparado ao estado da arte, o INS tem desempenho similar às medidas de quantificação de recorrência (análise dinâmica não linear), atingindo mais de 91% de acurácia [7]. Por outro lado, trabalhos utilizando medidas lineares (tais como frequência fundamental, jitter e shimmer) [17] e medidas não lineares baseadas em espaço de fase [25] chegaram a uma acurácia de aproximadamente 70% na discriminação entre vozes saudáveis e patológicas.…”