Parkinson's State Research Articles

Hybrid grey wolf and whale optimization for enhanced Parkinson's prediction based on machine learning models using biomedical sound

BackgroundBiomedical voice measurements have been used by many physicians and scientists to distinguish Parkinson's patients from ordinary people. Measurements of biomedical voices involve many variables calculated from signal analysis of the voice. These variables can be used to distinguish Parkinson's patients from non-Parkinson's patients. Unfortunately, using all computed variables may be ineffective and inaccurate due to the complexity of establishing a relationship between all the input variables and Parkinson's states. MethodsThis paper describes the development of a hybrid optimizer by combining two optimization algorithms: the Grey Wolf Optimizer and the Whale Optimizer. The hybrid optimizer enhances feature selection to provide a fast and robust Parkinson's prediction model. Additionally, this research incorporates five other feature selection algorithms for comparison purposes: Ranker, Greedy, BestFirst, Hybrid Grey Wolf Optimization, and Whale Optimization. The outputs from these algorithms are fed into six prediction models to determine the most accurate combination. These models include the neural network, Quest, Chi-squared Automatic Interaction Detection, support vector machine, CR-tree, and logistic regression models. Subsequently, the developed models are compared to identify the most accurate model based on various performance metrics. ResultsThe combination of the hybrid grey wolf and whale models yielded the highest scores in most metrics, achieving a perfect recall and a high F1 score. All models generated similar output, with an accuracy greater than 0.89. Quest, CR-tree, and neural networks are the most reliable and accurate models. ConclusionsBiomedical sound measurements can be used to develop an accurate and cost-effective Parkinson's prediction model.

Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm

Biophysical models contain a large number of parameters, while the spiking characteristics of neurons are related to a few key parameters. For thalamic neurons, relay reliability is an important characteristic that affects Parkinson's state. This paper proposes a method to fit key parameters of the model based on the spiking characteristics of neurons, and improves the traditional particle swarm optimization algorithm. That is, a nonlinear concave function and a Logistic chaotic mapping are combined to adjust the inertia weight of particles to avoid the particle falling into a local optimum in the search process or appearing premature convergence. In this paper, three parameters that play an important role in Parkinson's state of the thalamic cell model are selected and fitted by the improved particle swarm optimization algorithm. Using the fitted parameters to reconstruct the neuron model can predict the spiking trajectories well, which verifies the effectiveness of the fitting method. By comparing the fitting results with other particle swarm optimization algorithms, it is shown that the proposed particle swarm optimization algorithm can better avoid local optima and converge to the optimal values quickly.

Study of Discharge Outlines of Subthalamic Nucleus-Globus Pallidus External and the Favorable Impact of Deep Brain Stimulation in the Parkinson State

Aims: The aim of our model is to simulate deep brain stimulation mathematically instead of doing it experimentally to check whether the outlines in Parkinson’s disease helps in reducing the motor symptoms or not. The model is simulated here mathematically because clinical research is not feasible. My model is STN-GPe in which there are different differential equations. Also, to prove that the symptoms for Parkinson’s disease are reduced up to 99.59%.
 Study Design: Cross-correlation function.
 Place and duration of Study: Study was conducted in private college of Gurugram from January 2021 to September 2021.
 Methodology: Self designed, the conduct of the model has been accessible with the support of activity outlines produced in a normal state and Parkinson's state. These outlines have been related by finding out their correlation coefficient for dissimilar values of essential parameters. Here, a single-compartment conductance-based model is taken that concentrates on the subthalamic nucleus and synaptic input from globus pallidus (external). Current model displays extremely nonlinear behavior regarding many essential parameters.
 Results: Outcomes show that the activity outlines are extremely penetrating to several essential parameters. Deep brain stimulation is measured as a harmless and well-borne surgical technique to lighten Parkinson’s disease and extra movement ailments indications across some psychiatric situations. So, the deep brain stimulation technique has been applied here mathematically to find the cross-correlation between the two states. With the help of this stimulation, some promising results are obtained.
 Conclusion: The conclusion is that DBS is operative at reducing motor indications of parkinsonism by 99.59%.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.