Efficiency of Voice Features Based on Consonant for Detection of Parkinson's Disease

Puzhavakkathu Madom Viswanatha, R, Khojasteh, P, Aliahmad, B, Poosapadi Arjunan, S, Ragnav, S, Kempster, P, Wong, K, Nagao, J and Kumar, D 2018, 'Efficiency of Voice Features Based on Consonant for Detection of Parkinson's Disease', in Proceedings of the 2nd IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28-30 October 2018, pp. 49-52.


Document type: Conference Paper
Collection: Conference Papers

Title Efficiency of Voice Features Based on Consonant for Detection of Parkinson's Disease
Author(s) Puzhavakkathu Madom Viswanatha, R
Khojasteh, P
Aliahmad, B
Poosapadi Arjunan, S
Ragnav, S
Kempster, P
Wong, K
Nagao, J
Kumar, D
Year 2018
Conference name LSC 2018
Conference location Montreal, Quebec, Canada
Conference dates 28-30 October 2018
Proceedings title Proceedings of the 2nd IEEE Life Sciences Conference (LSC 2018)
Publisher IEEE
Place of publication United States
Start page 49
End page 52
Total pages 4
Abstract The objective of the study is to determine the efficiency of features extracted from sustained voiced consonant /m/ in the diagnosis of Parkinson's Disease (PD). The diagnostics applicability of the phonation /m/ is also compared with that of sustained phonation /a/, the one which is commonly employed in PD speech studies. The study included 40 subjects out of which 18 were PD and 22 were controls. The features extracted were used in SVM classifier model to differentiate PD and healthy subjects. The phonation /m/ yielded classification accuracy of 93% and Matthews Correlation Coefficient (MCC) of 0.85 while the classification accuracy for phonation /a/ was 70% and MCC of 0.39. The spearman correlation coefficient analysis also showed that the features from /m/ phonation were highly correlated with the Unified Parkinson's Disease Rating Scale (UPDRS-III) motor score. The results suggest the applicability of features corresponding to nasal consonant in the diagnosis and progression monitoring of PD.
Subjects Biomedical Engineering not elsewhere classified
Signal Processing
Keyword(s) Parkinson's Disease
Voice features
Consonant
Vowel
DOI - identifier 10.1109/LSC.2018.8572266
Copyright notice © 2018 IEEE
ISBN 9781538667095
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