Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people.

[1]  Nelia Lasierra,et al.  Lessons learned after a three-year store and forward teledermatology experience using internet: Strengths and limitations , 2012, Int. J. Medical Informatics.

[2]  Mireia Farrús,et al.  Jitter and shimmer measurements for speaker recognition , 2007, INTERSPEECH.

[3]  M. Breteler,et al.  Epidemiology of Parkinson's disease , 2006, The Lancet Neurology.

[4]  G. Grimmett,et al.  Probability and random processes , 2002 .

[5]  D. Lovinger,et al.  Conditional Expression of Parkinson's Disease-Related Mutant α-Synuclein in the Midbrain Dopaminergic Neurons Causes Progressive Neurodegeneration and Degradation of Transcription Factor Nuclear Receptor Related 1 , 2012, The Journal of Neuroscience.

[6]  Indrajit Mandal,et al.  Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system , 2013, Int. J. Medical Informatics.

[7]  Gang Wang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..

[8]  Mallikarjuna Rao,et al.  Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm , 2011 .

[9]  J. Jankovic.  Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[10]  Jack J. Jiang,et al.  Phonatory impairment in Parkinson's disease: evidence from nonlinear dynamic analysis and perturbation analysis. , 2007, Journal of voice : official journal of the Voice Foundation.

[11]  Sameer Malhotra,et al.  Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing , 2012, Int. J. Medical Informatics.

[12]  Matthias Scholz,et al.  Nonlinear Principal Component Analysis: Neural Network Models and Applications , 2008 .

[13]  J. Langston,et al.  Parkinson's disease: current and future challenges. , 2002, Neurotoxicology.

[14]  Finn Kensing,et al.  Designing for collaborative interpretation in telemonitoring: Re-introducing patients as diagnostic agents , 2011, Int. J. Medical Informatics.

[15]  J. Logemann,et al.  Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients. , 1978, The Journal of speech and hearing disorders.

[16]  H Hermansky,et al.  Perceptual linear predictive (PLP) analysis of speech. , 1990, The Journal of the Acoustical Society of America.

[17]  Shushan Zhao,et al.  Meta-analysis of association between PITX3 gene polymorphism and Parkinson's disease , 2012, Journal of the Neurological Sciences.

[18]  R Iansek,et al.  Speech impairment in a large sample of patients with Parkinson's disease. , 1999, Behavioural neurology.

[19]  L. H. Anauer,et al.  Speech Analysis and Synthesis by Linear Prediction of the Speech Wave , 2000 .

[20]  Bernard Teston,et al.  La dysarthrie dans la maladie de Parkinson , 2007 .

[21]  Ayyoob Jafari,et al.  CLASSIFICATION OF PARKINSON'S DISEASE PATIENTS USING NONLINEAR PHONETIC FEATURES AND MEL-FREQUENCY CEPSTRAL ANALYSIS , 2013 .

[22]  A. Hammouch,et al.  VOICE ANALYSIS FOR DETECTING PERSONS WITH PARKINSON’S DISEASE USING PLP AND VQ , 2014 .

[23]  L. Gavidia-Ceballos,et al.  A nonlinear operator-based speech feature analysis method with application to vocal fold pathology assessment , 1998, IEEE Transactions on Biomedical Engineering.

[24]  Fikret S. Gürgen,et al.  Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings , 2013, IEEE Journal of Biomedical and Health Informatics.

[25]  A. Benba,et al.  Voiceprint analysis using Perceptual Linear Prediction and Support Vector Machines for detecting persons with Parkinson ’ s disease , 2014 .

[26]  A. Benba,et al.  Hybridization of best acoustic cues for detecting persons with Parkinson's disease , 2014, 2014 Second World Conference on Complex Systems (WCCS).

[27]  Héctor M. Pérez Meana,et al.  Speaker recognition using Mel frequency Cepstral Coefficients (MFCC) and Vector quantization (VQ) techniques , 2012, CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers.

[28]  Steve Young,et al.  The HTK book version 3.4 , 2006 .

[29]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[30]  T P Speed,et al.  A DNA resequencing array for genes involved in Parkinson's disease. , 2012, Parkinsonism & related disorders.

[31]  Mark D Skowronski,et al.  Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition. , 2004, The Journal of the Acoustical Society of America.

[32]  R. Wielgat,et al.  HFCC based recognition of bird species , 2007, Signal Processing Algorithms, Architectures, Arrangements, and Applications SPA 2007.

[33]  Lorene M Nelson,et al.  Incidence of Parkinson's disease: variation by age, gender, and race/ethnicity. , 2003, American journal of epidemiology.

[34]  D. Huse,et al.  Burden of illness in Parkinson's disease , 2005, Movement disorders : official journal of the Movement Disorder Society.

[35]  Ahmed Hammouch,et al.  Detecting patients with Parkinson's disease using PLP and VQ , 2015, ICIT 2015.

[36]  J. Juillard.  [Parkinson disease]. , 1985, Revue de l'infirmiere.

[37]  Jennifer L. Spielman,et al.  Effects of intensive voice treatment (the Lee Silverman Voice Treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: acoustic and perceptual findings. , 2007, Journal of speech, language, and hearing research : JSLHR.

[38]  Ahmed Hammouch,et al.  Detecting Patients with Parkinson ’ s disease using Mel Frequency Cepstral Coefficients and Support Vector Machines , 2015 .

[39]  Zheng Rong Yang,et al.  RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins , 2005, Bioinform..

[40]  Paul Boersma,et al.  Praat, a system for doing phonetics by computer , 2002 .

[41]  Yonggang Yan,et al.  LASER PROJECTION SYSTEM TO LOCALIZE ACUPOINTS ON FACE USING 3D HEAD MODEL , 2013 .

[42]  Xi-tuan Ji,et al.  Effect of Bilateral Deep Brain Stimulation of the Subthalamic Nucleus on Freezing of Gait in Parkinson's Disease , 2012, The Journal of international medical research.

[43]  Hynek Hermansky,et al.  RASTA-PLP speech analysis technique , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[44]  A. Benba,et al.  Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease , 2015, 2015 International Conference on Electrical and Information Technologies (ICEIT).