A Novel Framework of Two Successive Feature Selection Levels Using Weight-Based Procedure for Voice-Loss Detection in Parkinson’s Disease

A Novel Framework of Two Successive Feature Selection Levels Using Weight-Based Procedure for Voice-Loss Detection in Parkinson’s Disease

Parkinson's disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a nove...

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Bibliographic Details
Main Authors: Amira S. Ashour, Majid Kamal A. Nour, Kemal Polat, Yanhui Guo, Wafaa Alsaggaf, Amira El-Attar
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Parkinson’s disease
voice loss
feature extraction
feature selection
principal component analysis
eigenvector centrality feature selection
Online Access:https://ieeexplore.ieee.org/document/9072452/

Internet

https://ieeexplore.ieee.org/document/9072452/

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