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T Vörös¹, Z Keresztényi, Cs Fazekas

¹Department of Bioengineering, Research Institute for Technical Physics and Material Sciences, Budapest, Hungary.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

This study introduces a software tool for objective Parkinson's Disease (PD) diagnosis. Mouse movement data analysis, using median and standard deviation, aids in tracking daily patient status changes.

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Area of Science:

Neurology
Biomedical Engineering
Computer Science

Background:

Parkinson's Disease (PD) diagnosis relies on clinical symptoms, often subjective.
Objective, quantifiable methods are needed for early detection and monitoring of PD progression.
Digital tools offer potential for standardized and accessible diagnostic support.

Purpose of the Study:

To develop and validate a software tool for objective Parkinson's Disease diagnosis.
To identify key parameters from hand exercise data for tracking PD patient status.
To leverage machine learning for improved diagnostic support in PD.

Main Methods:

Patients performed hand exercises using a computer mouse, generating movement data.
Analysis of various data parameters, focusing on median and standard deviation.

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Application of Self-Organizing Feature Maps (SOFM) for data classification and diagnostic support.

Main Results:

A specific parameter vector (median and standard deviation) was identified as effective for tracking daily changes in PD patients.
The software tool demonstrated capability in supporting the objective diagnosis of Parkinson's Disease.
SOFM classification provided valuable insights for the diagnostic process.

Conclusions:

The developed software tool offers a promising approach for objective Parkinson's Disease diagnosis.
Parameter vectors derived from mouse-based hand exercises can effectively monitor PD status.
Integrating machine learning, like SOFM, enhances diagnostic accuracy and patient management for PD.