Skip navigation
Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/42677
Title: Classification of ALS patients based on acoustic analysis of sustained vowel phonations
Authors: Vashkevich, M.
Rushkevich, Yu.
Keywords: публикации ученых;voice pathology detection;amyotrophic lateral sclerosis;voice quality
Issue Date: 2021
Publisher: Elsevier
Citation: Vashkevich, M. Classification of ALS patients based on acoustic analysis of sustained vowel phonations / M. Vashkevich, Yu. Rushkevich // Biomedical Signal Processing and Control. – 2021. – Vol. 65. – Р. 1–32. – DOI: https://doi.org/10.1016/j.bspc.2020.102350.
Abstract: Amyotrophic lateral sclerosis (ALS) is incurable neurological disorder with rapidly progressive course. Common early symptoms of ALS are difficulty in swallowing and speech. However, early acoustic manifestation of speech and voice symptoms is very variable, that making their detection very challenging, both by human specialists and automatic systems. This study presents an approach to voice assessment for automatic system that separates healthy people from patients with ALS. In particular, this work focus on analysing of sustain phonation of vowels /a/ and /i/ to perform automatic classification of ALS patients. A wide range of acoustic features such as MFCC, formants, jitter, shimmer, vibrato, PPE, GNE, HNR, etc. were analysed. We also proposed a new set of acoustic features for characterizing harmonic structure of the vowels. Calculation of these features is based on pitch synchronized voice analysis. A linear discriminant analysis (LDA) was used to classify the phonation produced by patients with ALS and those by healthy individuals. Several algorithms of feature selection were tested to find optimal feature subset for LDA model. The study’s experiments show that the most successful LDA model based on 32 features picked out by LASSO feature selection algorithm attains 99.7% accuracy with 99.3% sensitivity and 99.9% specificity. Among the classifiers with a small number of features, we can highlight LDA model with 5 features, which has 89.0% accuracy (87.5% sensitivity and 90.4% specificity).
URI: https://libeldoc.bsuir.by/handle/123456789/42677
Appears in Collections:Публикации в зарубежных изданиях

Files in This Item:
File Description SizeFormat 
Vashkevich_Classification.pdf4.04 MBAdobe PDFView/Open
Show full item record


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.