Computer-Aided Diagnosis of Speech Disorder Signal in Parkinson’s Disease
DOI:
https://doi.org/10.24297/ijct.v15i9.730Keywords:
Computer-aided diagnosis, Parkinson’s disease, Speech and voice disorders, Classification.Abstract
Computer-aided diagnosis (CAD) can be used as a decision support system by physicians in the diagnosis and treatment
of disordered speech especially those who specialize in neurophysiology diseases. Parkinson's disease (PD) is a
progressive disorder of the nervous system that affects movement. It develops gradually, sometimes starting with a barely
noticeable tremor in speech. It has been found that 80% of persons with PD reported speech and voice disorders.
Parkinson's disease symptoms worsen as the condition progresses over time. Therefore, Speech may become soft or
slurred and these deficits in speech intelligibility impact on health status and quality of life. Different researchers are
currently working in the analysis of speech signal of people with PD, including the study of different dimensions in speech
such as phonation, articulation, prosody, and intelligibility. Here, we present the characteristics and features of normal
speech and speech disorders in people with PD and the types of classification for implementation of the efficacy of
treatment interventions. The results show that our classification algorithm using ANN is outperformed KNN and SVM. ANN
is a practical and useful as a predictive tool for PD screening with a high degree of accuracy, approximately 96.1% of a
correct detection rate (sensitivity 94.7%, and specificity 96.6%). Based on the high levels of accuracy obtained by our
proposed algorithm, it can be used for enhancing the detection purpose to discriminate PD patients from healthy people.
Our algorithm may be used by the clinicians as a tool to confirm their diagnosis.
Downloads
References
and severity of dysarthria Journal of Speech, Language, and Hearing Research. 2001; (54):417–429.
[2] Kim, Y.-J., Weismer, G., Kent, R. D., & Duffy, J. R. Statistical models of F2 slope in relation to severity of dysarthria.
Folia Phoniatrica et Logopaedica. 2009; (61): 329–335.
[3] Rosen, K. M. Goozee, J. V., & Murdoch, B. E. Examining the effects of multiple sclerosis on speech production: Does
phonetic structure matter?. Journal of Communication Disorders.2008; (41):49–69.
[4]. A. K. Ho, R. Iansek, C. Marigliani, J. L. Bradshaw, and S. Gates. Speech impairment in a large sample of patients with
Parkinson's disease. Behavioural Neurology. 1998; 11(3):131–137.
[5] S. Skodda, W. Visser, and U. Schlegel. Gender-related patterns of dysprosody in Parkinson disease and correlation
between speech variables and motor symptomsâ€, Journal of Voice. 2011; 25(1):76–82.
[6] G. J. Canter.Speech characteristics of patients with Parkinson's disease. 3. Articulation, diadochokinesis, and over-all
speech adequacy. The Journal of Speech and Hearing Disorders. 1965; (30): 217–224.
[7] Schulz, G.M. & Grant, M.K. Effects of speech therapy and pharmacologic and surgical treatments on voice and speech
in Parkinson's disease: A review of the literature. Journal of Communication Disorders. 2000; 33(1): 59-88.
[8] Sharkawi .A, Ramig L, Logemann JA, et al. Swallowing and voice effects of Lee Silverman Voice Treatment (LSVT): A
pilot study. Journal of Neurology, Neurosurgery, and Psychiatry. 2002; 72: 31–36.
[9] Schwartz, R., et al. Speech Recognition in Multiple Languages and Domains: The 2003 BBN/LIMSI EARS System.
Proc. ICASSP, 2004.
[10] Tsanas A. et al. Novel speech signal processing algorithms for high accuracy classification of Parkinson’s disease.
IEEE Trans. Biomedical Engineering. 2012; 59(5):1264–1271.
[11] Khalid Daoudi, Blaise Bertrac. On classification between normal and pathological voices using the MEEI-KayPENTAX
database: Issues and consequences. INTERSPEECH, Jun 2014.
[12] Kaitlin L. Lansford, and Julie M. Liss, Vowel Acoustics in Dysarthria: Speech Disorder Diagnosis and Classification.
Journal of Speech, Language, and Hearing Research. February 2014; 57:57–67.
[13] J.R. Orozco-Arroyave, J.D. Arias-LondoËœno, J.F. Vargas-Bonilla. New Spanish speech corpus database for the
analysis of people suffering from Parkinson’s disease. Ninth International Conference on Language Resources and
Evaluation (LREC'14), At Reykjavik, Iceland, 2014.
[14] Yahia Alemami, Laiali Almazaydeh. Detection of Parkinson Disease through Voice Signal Features. Journal of
American Science, 2014; 10(10).
[15] Parkinson's disease dataset: https://archive.ics.uci.edu/ml/datasets/Parkinsons
[16] M.A. Little, P.E. McSharry, E.J. Hunter, J. Spielman, L.O. Ramig. Suitability of Dysphonia Measurements for
Telemonitoring of Parkinson’s Disease. IEEE Trans. Biomedical Engineering. 2009; 56(4):1015-1022.
[17] Balcázar, José. Computational Power of Neural Networks: A Kolmogorov Complexity Characterization. Information
Theory, IEEE Transactions. Jul 1997; 43(4): 1175–1183. Doi: 10.1109/18.605580.
[18] Altman, N. S. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician.
1992; 46 (3):175–185.
[19] Meyer, D.,Leisch, F.,Hornik, K. The support vector machine under test. Neurocomputing. 2003; 55: 169.
[20] A. M. Goberman. Correlation between acoustic speech characteristics and non-speech motor performance in
Parkinson disease. Med. Sci. Monit. 2005; 11:109–116.
[21] Zelcer S, Henri C, Tewfik TL, Mazer B. Multidimensional voice program analysis (MDVP) and the diagnosis of
pediatric vocal cord dysfunction. Ann Allergy Asthma Immunol. 2002; 88(6):601-8.
[22] John A. Rice. Mathematical Statistics and Data Analysis. Third Edition, Duxbury Advanced; 2006.
[23] Fogarty, James; Baker, Ryan S., Hudson, Scott E. Case studies in the use of ROC curve analysis for sensor-based
estimates in human computer interaction. ACM International Conference Proceeding Series, Proceedings of Graphics
Interface; 2005.
Downloads
Published
How to Cite
Issue
Section
