Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects the motor neurons of the brain and causes tremors, stiffness, and difficulty walking, balancing, and coordinating. Due to motor neuron loss, people with PD may suffer difficulties walking and talking as the disease develops. The changes in the vocal features in PD patients can be a significant metric for classifying Parkinson's disease for early prediction. Here we used some considerable vocal features such as jitter, shimmer, fundamental frequency parameters, Recurrence Period Density Entropy (RPDE), Noise/harmonic ratio (NHR), Pitch Period Entropy (PPE), Harmonics parameters, and Detrended Fluctuation Analysis (DFA) for Parkinson's disease prediction. In this paper, we applied ten different types of machine learning algorithms using vocal features for detecting PD and analyzed the machine learning performance among them. We applied XGBoost, LightGBM, Random Forest, Bagging, AdaBoost, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes classifiers for detecting PD. Result: According to our overall analysis, LightGBM achieved the highest accuracy of 95%, AUC of 96%, sensitivity of 100%, and specificity of 93.33%, respectively. Moreover, LightGBM outperformed other machine learning algorithms and was determined as the proposed model in terms of accuracy and AUC score.

Vocal Feature Guided Detection of Parkinson’s Disease Using Machine Learning Algorithms

Research on Passive Assessment of Parkinson's Disease Utilising Speech Biomarkers

OBJECTIVE
Deep brain stimulation (DBS) has considerable efficacy for the motor dysfunction of idiopathic Parkinson's disease (PD) on patient quality of life. However, the benefit of DBS on voice and speech quality remains controversial. We carried out a systematic review to understand the influence of DBS on parkinsonian dysphonia and dysarthria.


DATA SOURCES
A PubMed/MEDLINE and Cochrane systematic review was carried out following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population, Intervention, Comparison, Outcome, Timing, and Setting (PICOTS) statements.


REVIEW METHODS
Three investigators screened studies published in the literature from inception to May 2022. The following data were retrieved: age, demographic, sex, disease duration, DBS duration, DBS location, speech, and voice quality measurements.


RESULTS
From the 180 studies identified, 44 publications met the inclusion criteria, accounting for 866 patients. Twenty-nine studies focused on voice/speech quality in subthalamic DBS patients, and 6 included patients with stimulation of pallidal, thalamic, and zona incerta regions. Most studies (4/6) reported a deterioration of the vocal parameters on subjective voice quality evaluation. For speech, the findings were more contrasted. There was an important heterogeneity between studies regarding the voice and speech quality outcomes used to evaluate the impact of DBS on voice/speech quality.


CONCLUSION
The impact of DBS on voice and speech quality significantly varies between studies. The stimulated anatomical region may have a significant role since the stimulation of the pallidal area was mainly associated with voice quality improvement, in contrast with other regions. Future controlled studies comparing all region stimulation are needed to get reliable findings.


LEVEL OF EVIDENCE
Level III: evidence from evidence summaries developed from systematic reviews.

L. Carpentier

Papers

Deep Brain Stimulation Impact on Voice and Speech Quality in Parkinson's Disease: A Systematic Review.

The interplay between sleep quality and adolescents’ psychosocial development: a systematic review and meta-analysis of longitudinal studies