Application of Wavelet Analysis and Paraconsistent Feature Extraction in the Classification of Voice Pathologies

This study explores the application of wavelet analysis and paraconsistent logic for the classification of voice pathologies. The primary objective is to develop a methodology combining signal decomposition techniques and intelligent classification to distinguish between healthy and pathological voice samples.

Voice signals from the Saarbruecken Voice Database were preprocessed and decomposed using the discrete-time wavelet packet transform across multiple levels. Features such as energy, entropy, and zero-crossing rate (ZCR) were extracted for classification using support vector machines. Additionally, a paraconsistent logic framework was implemented to handle uncertainty and class overlap, enhancing classification. Six wavelet families were analyzed, including Haar, Daubechies, Symlets, Coiflets, Beylkin, and Vaidyanathan, to identify the most suitable filters for each pathology.

The proposed method achieved high classification accuracy, surpassing several state-of-the-art approaches. The best-performing filters varied by pathology, with Sym32, Beylkin18, and Vaidyanathan24 excelling for dysphonia, Daub4, Daub12, Sym8, and Coif6 for Reinke's edema, and Haar, Sym32, and Coif6 for recurrent laryngeal nerve paralysis. Energy and ZCR proved particularly effective as features, while entropy exhibited limited performance in this context.

The integration of wavelet-based signal analysis and paraconsistent logic offers a powerful approach for voice pathology classification. This methodology not only improves classification accuracy but also provides a computationally efficient framework suitable for clinical applications. Future work will focus on expanding datasets and developing real-time diagnostic tools.