A data ensemble-based approach for detecting vocal disorders using replicated acoustic biomarkers from electroglottography

Abstract

Introduction

The relevant prevalence of voice-related pathologies underscores the need for robust computer-aided diagnostic (CAD) systems capable of supporting early detection and continuous monitoring. Electroglottography (EGG), a non-invasive technique measuring vocal fold contact area, has proven valuable in identifying and diagnosing vocal disorders.

Problem statement

Traditional diagnostic methods struggle with the dependent nature of EGG measurements within subjects, leading to challenges in managing within-subject variability and supporting multi-class classification.

Objectives

This study aims to design, implement, and evaluate two ensemble-based approaches that address the dependency in EGG measurements. The goal is to enhance the detection of vocal disorders by managing within-subject variability and facilitating multi-class classification.

Methods

The proposed methods utilize replicated acoustic biomarkers derived from EGG signals. Simulation-based experiments were conducted to assess the robustness and effectiveness of these methods. Additionally, experiments were performed using EGG signals from the Saarbrüecken Voice Database (SVD).

Results

Simulation results indicate that integrating replicated data improves accuracy rates compared to non-replicated models. Experiments on SVD demonstrated the robustness of the proposed methodology across different vowels in classifying healthy individuals, patients with laryngitis, and those with vocal fold paralysis.

Conclusion

The data ensemble-based approaches developed effectively manage the dependent nature of EGG measurements, enhancing the detection and classification of vocal disorders. These methods can be applied to other data types where replications play a key role. Future research should focus on collecting comprehensive EGG databases and further exploring multi-class classification methods to solidify EGG and machine learning as a valuable tool for non-invasive assessment of laryngeal function.