Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

Gullapalli, Ajay Sankar; Mittal, Vinay Kumar

doi:10.1007/978-981-16-4177-0_22

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…Some of them are published and accessible in the form of corpus description, fewer of them are published in the form of recordings. Some of them are accessible on request and the smallest group are freely accessible PD 16 and AD 17 databases that can be directly used for machine learning. Furthermore, we can divide speech databases into groups according to the type of tasks that are recorded during its creation.…”

Section: Background and Summarymentioning

confidence: 99%

EWA-DB, Slovak Database of Speech Affected by Neurodegenerative Diseases

Rusko,

Sabo,

Trnka

et al. 2023

Preprint

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A new Slovak speech database EWA-DB was created for research purposes aimed at early detection of neurodegenerative diseases from speech. It contains 1649 speakers performing various speech and language tasks, such as sustained vowel phonation, diadochokinesis, naming and picture description. The sample of speakers consists of individuals with Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, and healthy controls. In this article we describe the EWA-DB development process, the language and speech task selection, patient and healthy control recruitment, as well as the testing and recording protocol. The structure and content of the database and file formats are described in detail. We assume that the presented database could be suitable for the development of automatic systems predicting the diagnoses of Alzheimer’s disease, mild cognitive impairment, and Parkinson’s disease from language and speech features.

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Section: Background and Summarymentioning

confidence: 99%

EWA-DB, Slovak Database of Speech Affected by Neurodegenerative Diseases

Rusko,

Sabo,

Trnka

et al. 2023

Preprint

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“…The best accuracy reached 94%. Gullapalli and Mittal (2022) used various classifiers like Logistic Regression, SVM, KNN, CNN, Deep Neural Network, Boosting, Bagging, Random Forest, and illustrate a comparison on their accuracies, based on MFCC, JTFA, MDVP and TQTW as main features. To date, feature selection has been successfully used in medical applications, where it cannot only reduce dimensionality and but also help us understand the causes of a disease better ( Remeseiro and Bolon-Canedo, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset

Wang

Yan²,

Shao³

et al. 2022

Front. Aging Neurosci.

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Parkinson’s disease (PD) is a neurodegenerative disorder that negatively affects millions of people. Early detection is of vital importance. As recent researches showed dysarthria level provides good indicators to the computer-assisted diagnosis and remote monitoring of patients at the early stages. It is the goal of this study to develop an automatic detection method based on newest collected Chinese dataset. Unlike English, no agreement was reached on the main features indicating language disorders due to vocal organ dysfunction. Thus, one of our approaches is to classify the speech phonation and articulation with a machine learning-based feature selection model. Based on a relatively big sample, three feature selection algorithms (LASSO, mRMR, Relief-F) were tested to select the vocal features extracted from speech signals collected in a controlled setting, followed by four classifiers (Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Stochastic Gradient Descent) to detect the disorder. The proposed approach shows an accuracy of 75.76%, sensitivity of 82.44%, specificity of 73.15% and precision of 76.57%, indicating the feasibility and promising future for an automatic and unobtrusive detection on Chinese PD. The comparison among the three selection algorithms reveals that LASSO selector has the best performance regardless types of vocal features. The best detection accuracy is obtained by SGD classifier, while the best resulting sensitivity is obtained by LR classifier. More interestingly, articulation features are more representative and indicative than phonation features among all the selection and classifying algorithms. The most prominent articulation features are F1, F2, DDF1, DDF2, BBE and MFCC.

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