ArticleScientific reports2022
Phonemes based detection of parkinson's disease for telehealth applications.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Development and clinical translation of smartphone-based digital biomarkers for Parkinson's disease: a structured review.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Physiological classification of Parkinson's disease severity using multimodal speech biomarkers with a hybrid CNN-Mamba framework.Frontiers in physiology · 2026Article
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Clinical assessment and interpretation of dysarthria in ALS using attention based deep learning AI models.NPJ digital medicine · 2025Article
- Automatic speech analysis combined with machine learning reliably predicts the motor state in people with Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Vocal Feature Changes for Monitoring Parkinson's Disease Progression-A Systematic Review.Brain sciences · 2025Review
- Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.Scientific reports · 2025Article
- Pre-trained Convolutional Neural Networks Identify Parkinson's Disease from Spectrogram Images of Voice Samples.Research square · 2024Article
- Unveiling early signs of Parkinson's disease via a longitudinal analysis of celebrity speech recordings.NPJ Parkinson's disease · 2024Article
- In Silico Decoding of Parkinson's: Speech & Writing Analysis.Journal of clinical medicine · 2024Article
- Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for?Journal of Parkinson's disease · 2024Review
- Early detection of Parkinson's disease from multiple signal speech: Based on Mandarin language dataset.Frontiers in aging neuroscience · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Dysarthria is an early symptom of Parkinson's disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan's dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for PD.
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