Prediction of Parkinson's Disease using Principal Component Analysis and the Markov Chains

Rizvi, Syed Qasim Afser; Pin, Liu; Wang, Guojun; Arif, Muhammad

doi:10.1109/isci50694.2020.00015

Cited by 2 publications

(1 citation statement)

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“…Trained HMMs can be used for classification tasks to distinguish between PD and healthy control subjects based on their balance control data. Additionally, HMMs can predict the progression of PD stages or forecast the onset of specific motor symptoms [33]. Clinical applications [32]: The application of HMMs in analyzing balance control data for PD patients has significant clinical implications.…”

Section: State Of the Artmentioning

confidence: 99%

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

Safi,

Aly,

Kanj

et al. 2024

Bioengineering

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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.

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Section: State Of the Artmentioning

confidence: 99%