An Optimized Approach to Huntington’s Disease Detecting via Audio Signals Processing with Dimensionality Reduction

Abstract

Huntington's disease is a hereditary condition in which brain nerve cells rupture over time. This work proposes a new method for the detection of Huntington's disease using digitised voice signals by diseased and healthy volunteers while they were reading Lithuanian poems. In this approach, the produced features by voice signals suffer a dimensionality reduction to optimize the prediction stage. The performance evaluation regarded 186 speech exams and 24 volunteers, combining twelve audio signal feature extractors with classification models. The results indicate an excellent performance, reaching precision and accuracy over 99 percent with prediction time below 1 second. This approach shows promising results indicating its usability to improve the medical diagnosis via computer-aided diagnosis.