Nemuel D Pah1,2, Mohammod A Motin2,3, Peter Kempster4, Dinesh K Kumar2. 1. Electrical Engineering DepartmentUniversitas SurabayaSurabaya60293Indonesia. 2. School of EngineeringRMIT UniversityMelbourneVIC3000Australia. 3. Department of Electrical and Electronic EngineeringRajshahi University of Engineering and TechnologyRajshahi6204Bangladesh. 4. Monash HealthClaytonVIC3168Australia.
Abstract
BACKGROUND: Parkinson's disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture. METHOD: This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM). RESULTS: The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD-off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD-off versus PD-on achieved an AUC of 0.81. CONCLUSION: Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.
BACKGROUND: Parkinson's disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture. METHOD: This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM). RESULTS: The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD-off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD-off versus PD-on achieved an AUC of 0.81. CONCLUSION: Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.
Entities:
Keywords:
Dysarthria; Parkinson’s disease; drug response; sustained phonemes; voice analysis
Authors: Jan Rusz; Roman Cmejla; Tereza Tykalova; Hana Ruzickova; Jiri Klempir; Veronika Majerova; Jana Picmausova; Jan Roth; Evzen Ruzicka Journal: J Acoust Soc Am Date: 2013-09 Impact factor: 1.840
Authors: Matej Skorvanek; Jennifer G Goldman; Marjan Jahanshahi; Connie Marras; Irena Rektorova; Ben Schmand; Erik van Duijn; Christopher G Goetz; Daniel Weintraub; Glenn T Stebbins; Pablo Martinez-Martin Journal: Mov Disord Date: 2017-11-23 Impact factor: 10.338