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  1. Validity and Reliability Study of Bahasa Malaysia Version of Voice Handicap Index-10
    Ong FM, Husna Nik Hassan NF, Azman M, Sani A, Mat Baki M
    J Voice, 2019 Jul;33(4):581.e17-581.e23.
    PMID: 29793874 DOI: 10.1016/j.jvoice.2018.01.015
    OBJECTIVES: This study aimed to determine the validity and reliability of Bahasa Malaysia version of Voice Handicap Index-10 (mVHI-10).

    MATERIALS AND METHODS: This cross-sectional study was carried out in the Otorhinolaryngology, Head and Neck Surgery Department of Universiti Kebangsaan Malaysia Medical Centre (UKMMC) from June 2015 to May 2016. The mVHI-10 was produced following a rigorous forward and backward translation. One hundred participants, including 50 healthy volunteers (17 male, 33 female) and 50 patients with voice disorders (26 male, 24 female), were recruited to complete the mVHI-10 before flexible laryngoscopic examinations and acoustic analysis. The mVHI-10 was repeated in 2 weeks via telephone interview or clinic visit. Its reliability and validity were assessed using interclass correlation.

    RESULTS: The test-retest reliability for total mVHI-10 and each item score was high, with the Cronbach alpha of >0.90. The total mVHI-10 score and domain scores were significantly higher (P 

    Matched MeSH terms: Dysphonia/physiopathology
  2. A new hybrid intelligent system for accurate detection of Parkinson's disease
    Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Dysphonia/physiopathology*
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