Empirical mode decomposition. Spectral properties in normal and pathological voices

Abstract

Empirical Mode Decomposition is a data driven technique proposed by Huang. In this work, we explore spectral properties of the intrinsic mode functions and apply them to speech signals corresponding to real and simulated sustained vowels. For the synthetic sustained vowels we propose a phonation model that includes perturbations implied in common laryngeal pathologies. We extract features from each signal using the Burg’s standard spectral analysis of their intrinsic mode functions. Due to its well-known theoretical properties, the classic K-nearest neighbor’s classification rule is applied to real and synthetic data. We show that even using this basic pattern classification algorithm, the selected spectral features of only three intrinsic mode functions are enough to discriminate between normal and pathological voices. We have obtained a 99.00% of correct classifications between normal and pathological synthetic voices (K=1, sensitivity=0.990, specificity=0.990); while in the case of real voices the percentage of correct classification was 93.40% (K=3, sensitivity=0.925, specificity=0.926). These results strongly suggest that spectral properties of Empirical Mode Decomposition provide useful discriminative information for this task. Additionally we consider two pathologies of different etiology and treatment, which, given the similarity of their voice characteristics, are frequently misdiagnosed in clinical practice: muscular tension dysphonia and adductor spasmodic dysphonia. Preliminary results with a reduced real data base suggest that this approach could provide useful orientation to physicians and voice pathologists.