Hidden Markov Model for Parkinson's Disease Patients Using Balance Control Data
Khaled Safi1, Wael Hosny Fouad Aly2, Hassan Kanj2
1Computer Science Department, Jinan University, Tripoli P.O. Box 818, Lebanon.
Bioengineering (Basel, Switzerland)
|January 22, 2024
Summary
A novel hidden Markov model (HMM) accurately distinguishes Parkinson's disease (PD) patients from healthy individuals using raw stabilometric data. This approach achieves up to 98% accuracy, offering a promising tool for early detection and management of PD.
Area of Science:
- Biomechanics
- Neurology
- Data Science
Background:
- Human postural control is vital for balance in static and dynamic conditions.
- Parkinson's disease (PD) severely impairs stability, increasing fall risk.
- Objective assessment of PD-related postural deficits is crucial.
Purpose of the Study:
- To develop and validate a novel method for differentiating individuals with PD from healthy controls.
- To assess the efficacy of a hidden Markov model (HMM) using raw stabilometric data.
Main Methods:
- Utilized a hidden Markov model (HMM) for classification.
- Employed raw, preprocessed stabilometric signals from 60 participants (healthy and PD).
- Evaluated the model's accuracy in distinguishing between the two groups.
Main Results:
- The HMM achieved a high accuracy rate of up to 98% in differentiating healthy subjects from PD patients.
- The method demonstrated effectiveness using unprocessed stabilometric data.
- Successful distinction between healthy and PD groups was achieved.
Conclusions:
- The proposed HMM-based approach offers a highly accurate and efficient method for identifying Parkinson's disease.
- Utilizing raw stabilometric data simplifies the diagnostic process.
- This technique holds potential for non-invasive PD diagnosis and monitoring.
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