Application for acoustic assessment: A pilot study in Parkinson’s patients
Main Article Content
Abstract
Background: Patients with Parkinson’s disease (PD) often experience speech impairment that impacts their daily lives. In speech therapy for PD patients, it is important to use instruments to evaluate acoustic characteristics and support rehabilitation programs. However, in Thailand, access to acoustic assessment instruments is limited due to their high price and lack of portability.
Objectives: This study’s aim was to develop an application for acoustic assessment (AAA) and to conduct a pilot study of the application among healthy aging (HA) and aging PD subjects.
Materials and methods: This study was a developmental research design comprising three distinct phases. Phase one focused on the development of the AAA and evaluation of the accuracy and precision of the application. In phase two, a comparative analysis was conducted between AAA and Praat, a speech analysis software package, among twenty HA. Five acoustic parameters: loudness, jitter, shimmer, high frequency (Hf0), and low frequency (Lf0), were used to determine concurrent validity. Phase three compared both AAA and Praat with twenty aging PD subjects, examining the concurrent validity and reliability, and comparing the acoustic parameters of HA users with those of the aging PD cohort.
Results: In phase one, the AAA shows strong accuracy ranging from 96.86% to 99.59% and high precision, with a Coefficient of Variation (%CV) of 1.65%-3.78%. In phase two, the concurrent validity of AAA compared with Praat in HA exhibited significant and very strong correlations (rs ≥ 0.90, p > 0.05) in all acoustic parameters, except for shimmer, which showed significant and strong correlations (rs = 0.73, p = 0.00). In phase three, the concurrent validity of AAA compared with Praat in aging PD subjects exhibited significant and very strong correlations (rs ≥ 0.90, p > 0.05) in loudness, Hf0, and Lf0, whereas significant and strong correlations were shown in jitter (rs = 0.85, p = 0.00) and shimmer (rs = 0.82, p = 0.00). The Intraclass Correlation Coefficient (ICC) exhibited excellent reliability in all acoustic parameters (r > 0.90). When comparing the HA and aging PD subjects using AAA, significant differences (p < 0.05) were observed in all acoustic parameters, except for Lf0
(p < 0.55).
Conclusion: The AAA demonstrates high concurrent validity and reliability. It can effectively be utilized for testing in PD groups, serving as an alternative tool for evaluating acoustic characteristics and aiding in treatment planning.
Article Details
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Personal views expressed by the contributors in their articles are not necessarily those of the Journal of Associated Medical Sciences, Faculty of Associated Medical Sciences, Chiang Mai University.
References
Olanow CW, Stocchi F, Lang AE. The dopaminergic and non-dopaminergic features of Parkinson’s disease. In: Olanow CW, Stocchi F, Lang AE, editors. Parkinson’s disease. Chichester: Wiley-Blackwell; 2011. p. 1-6.
Rego AC, Cardoso SM, Oliveira CR. Molecular pathways of mitochondrial dysfunction in neurodegeneration: the paradigms of parkinson’s and huntington’s diseases. In: Malva JO, Rego AC, Cunha RA, Oliveira CR, editors. Interaction between neurons and glia in aging and disease. Boston: Springer; 2007. p. 193-219.
Muangpaisan W, Siritipakorn P, Assantachai P. Development of a Thai parkinson’s disease screening tool and the prevalence of parkinsonism and parkinson’s disease, based on a community survey in Bangkok. Neuroepidemiology. 2017; 49: 74-81. doi:10.1159/ 000480510.
Muñoz-Vigueras N, Prados-Román E, Valenza MC, Granados-Santiago M, Cabrera-Martos I, RodríguezTorres J, et al. Speech and language therapy treatment on hypokinetic dysarthria in parkinson disease: systematic review and meta-analysis. Clin Rehabil. 2020; 35: 639-55. doi:10.1177/0269215520976267.
Galáž Z, Mekyska J, Zvoncak V, Mucha J, Kiska T, Smekal Z, et al. Changes in phonation and their relations with progress of Parkinson’s disease. Appl Sci. 2018; 8: 2339. doi:10.3390/app8122339.
Duffy JR. Hypokinetic Dysarthria. In Duffy JR, editor. Motor speech disorders: substrates, differential diagnosis, and management. 3rd Ed. St. Louis: Elsevier /Mosby; 2013: p. 165-89.
Pinto S, Ozsancak C, Tripoliti E, Thobois S, LimousinDowsey P, Auzou P. Treatments for dysarthria in parkinson’s disease. Lancet Neurol. 2004; 3: 547-56. doi:10.1016/S1474-4422(04)00854-3.
Fox CM, Ramig LO. Vocal sound pressure level and self-perception of speech and voice in men and women with idiopathic parkinson disease. Am J Speech Lang Pathol. 1997; 6: 85-94. doi:10.1044/ 1058-0360.0602.85.
Rusz J, Cmejla R, Ruzickova H, Ruzicka E. Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated parkinson’s disease. J Acoust Soc Am. 2011; 129: 350-67. doi:10.1 121/1.3514381.
Suphinnapong P, Phokaewvarangkul O, Thubthong N, Teeramongkonrasmee A, Mahattanasakul P, Lorwattanapongsa P, et al. Objective vowel sound characteristics and their relationship with motor dysfunction in Asian parkinson’s disease patients. J Neurol Sci. 2021; 426: 117487. doi:10.1016/j.jns. 2021.117487.
Dejonckere PH, Bradley P, Clemente P, Cornut G, Crevier-Buchman L, Friedrich G, et al. A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques. Guideline elaborated by the committee on phoniatrics of the European Laryngological Society (ELS). Eur Arch Otorhinolaryngol. 2001; 258: 77-82. doi: 10. 1007/s004050000299.
Mehta DD, Hillman RE. Voice assessment: updates on perceptual, acoustic, aerodynamic, and endoscopic imaging methods. Curr Opin Otolaryngol Head Neck Surg. 2008; 16: 211-5. doi:10.1097/MOO.0b013e3282fe96ce.
Patel RR, Awan SN, Barkmeier-Kraemer J, Courey M, Deliyski D, Eadie T, et al. Recommended protocols for instrumental assessment of voice: American speech-language-hearing association expert panel to develop a protocol for instrumental assessment of vocal function. Am J Speech Lang Pathol. 2018; 27: 887-905. doi:10.1044/2018_AJSLP-17-0009.
Chiaramonte R, Bonfiglio M. Acoustic analysis of voice in parkinson’s disease: a systematic review of voice disability and meta-analysis of studies. Rev Neurol. 2020; 70: 393-405. doi:10.33588/rn.7011.2019414.
Maryn Y. Practical acoustics in clinical voice assessment: a Praat primer. Perspect ASHA Spec Interest Groups. 2017; 2: 14-32. doi:10.1044/persp2. SIG3.14.
Eyben F, Weninger F, Gross F, Schuller B. Recent developments in openSMILE, the munich opensource multimedia feature extractor. In: Jaimes A, Sebe N, Boujemaa N, editors. Proceedings of the 21st ACM international conference on multimedia; 2013 Oct 21-25. Barcelona, Spain. New York: Association for Computing Machinery; 2013. p. 835-53.
Department of Mental Health. Guidebook of depressive disorders surveillance and care: provincial level. 3rd Ed, Rev. Ubon Ratchathani: Department of Mental Health; 2014. (in Thai)
Prasert Boongird. Interesting story about dementia [internet]. 2018 [cited 2024 May 14]. Available from: https://w1.med.cmu.ac.th/anes/wp-content/uploads/ 2020/10/Newsletter_ MSET10-2.pdf. (in Thai)
IBM. How to cite IBM SPSS Statistics or earlier versions of SPSS 2014 [internet]. 2014 [cited 2024 May 24]. Available from: https://www.ibm.com/support/ pages/how-citeibm-spss-statistics-or-earlier-versions-spss.
Navaphattra Nunak, Taweepol Suesut. Measurement and instrumentation: application in the food industry. Bangkok: KMITL School of Engineering; 2012. (in Thai)
Hinkle DE, Wiersma W, Jurs SG. Applied statistics for the behavioral sciences 3rd Ed. Boston, MA: Houghton Mifflin; 1994
Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016; 15: 155-63. doi:10.1016/j.jcm. 2016.02.012.
Hazra A. Using the confidence interval confidently. J Thorac Dis. 2017; 9: 4124-9. doi:10.21037/jtd.2017. 09.14.
Maryn Y, Roy N, De Bodt M, Van Cauwenberge P, Corthals P. Acoustic measurement of overall voice quality: a meta-analysis. J Acoust Soc Am. 2009; 126: 2619-34. doi:10.1121/1.3224706.
Vaz-Freitas S, Pestana PM, Almeida V, Ferreira A. Acoustic analysis of voice signal: comparison of four applications software. Biomed Signal Process Control. 2018; 40: 318-23. doi:10.1016/j.bspc.2017.09.031.
Trail M, Fox C, Ramig LO, Sapir S, Howard J, Lai EC. Speech treatment for parkinson’s disease. Neuro Rehabilitation. 2005; 20: 205-21. doi:10.3233/NRE2005-20307.