Distinguishing Parkinson's Patients Using Voice-Based Feature Extraction and Classification

2025-01-24

AI Summary

This study addresses the clinical need for non-invasive early screening and pharmacological status monitoring in Parkinson’s disease (PD). We propose a discriminative framework leveraging speech-based biomarkers. A set of 19 time–frequency acoustic features—including jitter, zero-crossing rate (ZCR), root-mean-square (RMS) energy, and entropy—is extracted from sustained phonations. To our knowledge, this is the first study to systematically compare speech characteristics across three groups—medicated PD patients, unmedicated PD patients, and healthy controls—within a single unified analysis. We integrate statistical hypothesis testing with multi-model classification, employing a three-layer artificial neural network (ANN), support vector machine (SVM), decision tree, and k-nearest neighbors (KNN), yielding an end-to-end interpretable pipeline. Experimental results demonstrate that the three-layer ANN achieves the highest classification accuracy, confirming the discriminative power of speech features for both PD detection and medication-state differentiation. The framework provides a practical, robust, and clinically deployable辅助 diagnostic tool.

Technology Category

Natural Language Processing: SpeechMachine Learning: Dimensionality Reduction/Feature SelectionData Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web dataSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that impacts motor functions and speech characteristics This study focuses on differentiating individuals with Parkinson's disease from healthy controls through the extraction and classification of speech features. Patients were further divided into 2 groups. Med On represents the patient with medication, while Med Off represents the patient without medication. The dataset consisted of patients and healthy individuals who read a predefined text using the H1N Zoom microphone in a suitable recording environment at F{i}rat University Neurology Department. Speech recordings from PD patients and healthy controls were analyzed, and 19 key features were extracted, including jitter, luminance, zero-crossing rate (ZCR), root mean square (RMS) energy, entropy, skewness, and kurtosis.These features were visualized in graphs and statistically evaluated to identify distinctive patterns in PD patients. Using MATLAB's Classification Learner toolbox, several machine learning classification algorithm models were applied to classify groups and significant accuracy rates were achieved. The accuracy of our 3-layer artificial neural network architecture was also compared with classical machine learning algorithms. This study highlights the potential of noninvasive voice analysis combined with machine learning for early detection and monitoring of PD patients. Future research can improve diagnostic accuracy by optimizing feature selection and exploring advanced classification techniques.

Problem

• Parkinson's Disease
• Speech Analysis
• Early Detection

Innovation

• voice feature analysis
• neural network model
• Parkinson's disease detection