Acoustic and hearing-perceptual voice analysis in individuals with idiopathic Parkinson's disease in "on" and "off" stages

Biomechanical Parameters of Voice in Parkinson's Disease Patients

INTRODUCTION

Previous research on voice in Parkinson's disease (PD) has consistently demonstrated alterations in acoustic parameters, including fundamental frequency (F0), maximum phonation time, Shimmer, and Jitter. However, investigations into acoustic parameter alterations in individuals with PD are limited.

METHODS

We conducted an experimental study involving 20 PD patients (six women and fourteen men). Subjective measures of voice (VHI-30 scale and GRBAS) and objective measures using the OnlineLAB App tool for analyzing biomechanical correlates of voice were recorded. The app analyzed a total of 22 biomechanical parameters of voice.

RESULTS

The results of subjective measures were consistent with findings from previous studies. However, the results of objective measures did not align with studies that employed acoustic measures.

CONCLUSIONS

The biomechanical analysis revealed alterations in various parameters according to gender. These findings open up a new avenue of research in voice analysis for patients with PD, whether through acoustic or biomechanical analysis, aiming to determine whether the observed changes in these patients' voices are attributable to age or disease progression. This line of investigation will help elucidate the relative contribution of these factors to vocal alterations in PD patients and provide a more comprehensive understanding of the underlying mechanisms.

Acoustical and Perceptual Analysis of Voice in Individuals with Parkinson's Disease

Parkinson's disease is a neurodegenerative disorder that affects motor efficiency which is also required for voice production. Voice is part of the identity of a person, any abnormality in voice quality hampers the quality of communication, and quality of life. This study aimed to analyse the voice of individuals with Parkinson's disease objectively through acoustic analysis, and subjectively through perceptual analysis. Quasi-experimental study conducted in a tertiary health care centre. The study comprised two groups: 12 individuals with Parkinson's disease (11 males and 1 female, mean age: 72.41 years), and 12 healthy controls (10 males and 2 females, mean age: 53.83 years). The voice samples of all the participants were recorded and analyzed using the MDVP software of CSL 4500. The voice samples were analyzed for eleven acoustical parameters, and the perceptual analysis was carried out using the GRBAS scale by two experienced Speech Language Pathologists. Mann-Whitney U test was performed to compare the two groups of participants, and Cronbach's alpha test was performed to find the inter-judge reliability between the perceptual ratings of two listeners. Acoustical comparison showed significant variations in seven parameters (jitter, shimmer, PPQ, APQ, Fatr Hz, Fftr, ATRI), and the perceptual analysis between two listeners showed a fair amount of reliability.

Voice Analysis to Differentiate the Dopaminergic Response in People With Parkinson's Disease

Humans' voice offers the widest variety of motor phenomena of any human activity. However, its clinical evaluation in people with movement disorders such as Parkinson's disease (PD) lags behind current knowledge on advanced analytical automatic speech processing methodology. Here, we use deep learning-based speech processing to differentially analyze voice recordings in 14 people with PD before and after dopaminergic medication using personalized Convolutional Recurrent Neural Networks (p-CRNN) and Phone Attribute Codebooks (PAC). p-CRNN yields an accuracy of 82.35% in the binary classification of ON and OFF motor states at a sensitivity/specificity of 0.86/0.78. The PAC-based approach's accuracy was slightly lower with 73.08% at a sensitivity/specificity of 0.69/0.77, but this method offers easier interpretation and understanding of the computational biomarkers. Both p-CRNN and PAC provide a differentiated view and novel insights into the distinctive components of the speech of persons with PD. Both methods detect voice qualities that are amenable to dopaminergic treatment, including active phonetic and prosodic features. Our findings may pave the way for quantitative measurements of speech in persons with PD.

Objective vowel sound characteristics and their relationship with motor dysfunction in Asian Parkinson's disease patients

BACKGROUND

Speech impairments are very common in patients with Parkinson's disease (PD). However, knowledge of their objective characteristics and relationship to other motor symptoms amongst Asian PD patients is limited.

OBJECTIVES

To identify objective vowel sound characteristics in Thai PD patients and correlate with disease severity, as determined by UPDRS and various sub-scores.

METHOD

We evaluated 100 Thai PD patients, with a mean age of 66.56 years (±7.52) and HY of 2.7 (±1.08), and 101 age-matched controls. Phonatory evaluation, comprising of 15 objective parameters, was conducted using the Multi-Dimensional Voice Programme with a sustained /a/ phonation.

RESULTS

PD patients exhibited significantly higher values of all dimensions of the phonatory parameters evaluated compared to controls (All, p < 0.001) except for duration of sustained phonation, which was significantly shorter in PD patients. When early- and advanced-stage patients were compared, significantly different parameters were limited to frequency perturbation parameters (Jitt, p = 0.01; RAP, p = 0.013; PPQ, p = 0.01; sPPQ, p = 0.001; vF0, p = 0.011), and NHR (p = 0.028). Several significant and moderate correlations were observed between both STD and frequency perturbation parameters and UPDRS-III, bradykinesia sub-score, and gait and postural instability sub-score. Both vF0, and STD significantly correlated with UPDRS-III and sub-scores in advanced stage patients.

CONCLUSION

Our study provides objective evidence of phonatory dysfunction in Asian PD patients with certain characteristics correlated with advanced stage or different motor dysfunction. Sustained vowel phonation is a promising digital outcome for global phenotyping a large number of PD patients.

Detecting Effect of Levodopa in Parkinson's Disease Patients Using Sustained Phonemes

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.

Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia

It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria.

Dopaminergic Treatment Effects on Dysarthric Speech: Acoustic Analysis in a Cohort of Patients With Advanced Parkinson's Disease

Importance: The effects of dopaminergic treatment on speech in patients with Parkinson's disease (PD) are often mixed and unclear. The aim of this study was to better elucidate those discrepancies. Methods: Full retrospective data from advanced PD patients before and after an acute levodopa challenge were collected. Acoustic analysis of spontaneous monologue and sustained phonation including several quantitative parameters [i.e., maximum phonation time (MPT); shimmer local dB] as well as the Unified Parkinson's Disease Rating Scale (UPDRS) (total scores, subscores, and items) and the Clinical Dyskinesia Rating Scale (CDRS) were performed in both the defined-OFF and -ON conditions. The primary outcome was the changes of speech parameters after levodopa intake. Secondary outcomes included the analysis of possible correlations of motor features and levodopa-induced dyskinesia (LID) with acoustic speech parameters. Statistical analysis included paired t-test between the ON and OFF data (calculated separately for male and female subgroups) and Pearson correlation between speech and motor data. Results: In 50 PD patients (male: 32; female: 18), levodopa significantly increased the MPT of sustained phonation in female patients (p < 0.01). In the OFF-state, the UPDRS part-III speech item negatively correlated with MPT (p = 0.02), whereas in the ON-state, it correlated positively with the shimmer local dB (p = 0.01), an expression of poorer voice quality. The total CDRS score and axial subscores strongly correlated with the ON-state shimmer local dB (p = 0.01 and p < 0.01, respectively). Conclusions: Our findings emphasize that levodopa has a poor effect on speech acoustic parameters. The intensity and location of LID negatively influenced speech quality.

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease

In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD-CO, PD-PD and CO-CO. Four features reported in the literature-normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)-were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO-PD and PD-PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD-CO and PD-PD is higher compared with CO-CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

Idiopathic Parkinson's disease: vocal and quality of life analysis

OBJECTIVE: To compare voice and life quality of male patients with idiopathic Parkinson's disease, with individuals without disease (Control Group). METHODS: A cross-sectional study that evaluated the voice of individuals with Parkinson's disease, the group was composed of 27 subjects, aged from 39 to 79 years-old (average 59.96). The Control Group was matched on sex and age. Participants underwent voice recording. Perceptual evaluation was made using GRBASI scale, which considers G as the overall degree of dysphonia, R as roughness, B as breathiness, A as asthenia, S as strain and I as instability. The acoustic parameters analyzed were: fundamental frequency, jitter, shimmer, and harmonic to noise ratio (NHR). For vocal self-perception analysis, we used the Voice Related Quality of Life protocol. RESULTS: Fundamental frequency and jitter presented higher values in the Parkinson's group. NHR values were higher in the Control Group. Perceptual analysis showed a deviation ranging. The vocal disorder self-perception demonstrated a worse impact on quality of life. CONCLUSIONS: Individuals with Parkinson's disease have an altered voice quality and a negative impact on quality of life.

Objective and subjective analysis of women's voice with idiopathic Parkinson's disease

OBJECTIVE: To compare the voice quality of women with idiopathic Parkinson's disease and those without it. METHODS: An evaluation was performed including 19 female patients diagnosed with idiopathic Parkinson's disease, with an average age of 66 years, and 27 women with an average of 67 years-old in the Control Group. The assessment was performed by computed acoustic analysis and perceptual evaluation. RESULTS: Parkinson's disease patients presented moderate rough and unstable voice quality. The parameters of grade, roughness, and instability had higher scores in Parkinson's disease patients with statistically significant differences. Acoustic measures of Jitter and period perturbation quotient (PPQ) significantly differ between groups. CONCLUSIONS: Parkinson's disease female individuals showed more vocal alterations compared to the Control Group, when both perceptual and acoustic evaluations were analyzed.