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

TL;DR

This study demonstrates that voice feature extraction combined with machine learning can effectively distinguish Parkinson's patients from healthy controls, offering a noninvasive approach for early diagnosis and monitoring.

Contribution

It introduces a novel application of speech features and machine learning algorithms to differentiate PD patients from healthy individuals with high accuracy.

Findings

Achieved high classification accuracy using neural networks and classical algorithms.

Identified distinctive speech patterns in PD patients through statistical analysis.

Validated the potential of voice analysis for noninvasive PD detection.

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.