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. 2024 Jan 17;11(1):88.
doi: 10.3390/bioengineering11010088.

Hidden Markov Model for Parkinson's Disease Patients Using Balance Control Data

Khaled Safi  1 , Wael Hosny Fouad Aly  2 , Hassan Kanj  2 , Tarek Khalifa  2 , Mouna Ghedira  3 , Emilie Hutin  3
Affiliations

Hidden Markov Model for Parkinson's Disease Patients Using Balance Control Data

Khaled Safi et al. Bioengineering (Basel). .

Abstract

Understanding the behavior of the human postural system has become a very attractive topic for many researchers. This system plays a crucial role in maintaining balance during both stationary and moving states. Parkinson's disease (PD) is a prevalent degenerative movement disorder that significantly impacts human stability, leading to falls and injuries. This research introduces an innovative approach that utilizes a hidden Markov model (HMM) to distinguish healthy individuals and those with PD. Interestingly, this methodology employs raw data obtained from stabilometric signals without any preprocessing. The dataset used for this study comprises 60 subjects divided into healthy and PD patients. Impressively, the proposed method achieves an accuracy rate of up to 98% in effectively differentiating healthy subjects from those with PD.

Keywords: HMM; Parkinson’s disease; machine learning; postural stability; stabilometric data.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
An example of a discrete hidden Markov model.
Figure 2
Figure 2
A sample of a Gaussian HMM.
Figure 3
Figure 3
Proposed classification approach diagram.
Figure 4
Figure 4
The log-likelihood maximization process of the HMMs using both AP and ML signals.
See this image and copyright information in PMC

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