Neuropulse: Detecting Parkinson’s Disease

Abstract

The study delves into the application of machine learning techniques to facilitate early detection of Parkinson\'s disease. It examines two distinct aspects: hand movements and vocal features. Unique datasets tracking the progressive changes in these factors over time are explored. Specialized techniques are employed to extract the most distinguishing hand motions and speech characteristics, which serve as potential biomarkers. Unlike traditional methods that rely solely on a single feature, this multi-modal approach combines both hand movement and voice biomarkers into a unified computational model. Overall, the research illustrates the promising potential of machine learning tools to enable earlier intervention for medical purposes, while emphasizing that the focus remains on aiding clinicians rather than replacing specialized assessments. The study does not aim at individual diagnosis but rather explores avenues for supporting healthcare professionals. Future research endeavors involve developing multi-modal models that encompass a broader range of aspects associated with this complex and variable condition.

Introduction

Parkinson’s disease (PD) is a complex neurodegenerative disorder characterized by motor and cognitive impairments, caused primarily by dopamine neuron degeneration and Lewy body accumulation. Beyond motor symptoms, PD affects speech and handwriting, offering potential diagnostic markers. Recent technological advances utilize machine learning to analyze these two key domains—hand movements and vocal patterns—for early PD detection.

This survey reviews existing studies that employ various machine learning techniques on handwriting (e.g., hand-drawn spirals) and voice data to identify PD with high accuracy, often exceeding 90%. Notable methods include deep learning models like CNNs for image analysis, logistic regression, and support vector machines for vocal features, and ensemble learning to handle incomplete data.

The proposed system integrates these two modalities—handwriting and speech—into a multimodal machine learning framework that extracts detailed features (kinematic in handwriting; acoustic in speech), applies advanced feature selection, and combines outputs through rule-based fusion. This approach provides a more holistic and accurate diagnosis than single-modality methods, achieving up to 95% accuracy on training data and promising generalization.

Applications include early screening, remote monitoring via telemedicine, personalized treatment, research on disease progression, public health risk assessment, and educational tools. However, further large-scale, real-world validation is necessary before clinical implementation.

Conclusion

In conclusion, this cutting-edge multimodal approach offers a pioneering solution for the early identification of Parkinson\'s disease (PD). By concurrently analyzing hand movements and speech patterns, this methodology creates a comprehensive dataset capturing the dynamic nature of PD across diverse severities. Rigorous preprocessing techniques and advanced feature engineering enable the extraction of valuable kinematic attributes from hand-drawn images and speech metrics, shedding light on fine motor control deterioration and early speech impairments associated with PD. The integration of these complementary biomarkers enhances accuracy and depth of insights, potentially revolutionizing PD diagnosis and early intervention strategies. This innovative approach marks a significant step forward in understanding and addressing the complexities of Parkinson\'s disease.

References

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Copyright

Copyright © 2025 Akhil D, B Mukthananda, D Vinay Kumar, Geetanandu T, Srinath Devale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.