ASCLSDJun 9, 2020

Vocal markers from sustained phonation in Huntington's Disease

arXiv:2006.05365v33 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the need for non-invasive, automatic markers to monitor neurodegenerative diseases like Huntington's Disease, though it is incremental as it builds on existing research into vocal biomarkers.

The study investigated whether vocal markers from sustained phonation could serve as clinical indicators for Huntington's Disease, finding that phonation alone is insufficient for identifying sub-clinical disorders in premanifest gene carriers, but phonatory features are suitable for predicting clinical performance.

Disease-modifying treatments are currently assessed in neurodegenerative diseases. Huntington's Disease represents a unique opportunity to design automatic sub-clinical markers, even in premanifest gene carriers. We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up. We used two sets of features: Phonatory features and Modulation Power Spectrum Features. We found that phonation is not sufficient for the identification of sub-clinical disorders of premanifest gene carriers. According to our regression results, Phonatory features are suitable for the predictions of clinical performance in Huntington's Disease.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes