A Novel Closed-Loop Deep Brain Stimulation Technique for Parkinson's Patients Rehabilitation Utilizing Machine Learning

Deep brain stimulation (DBS) is an effective treatment for movement problems caused by a variety of neurodegenerative illnesses, including Parkinson's disease (PD). Researchers have created closed-loop DBS (CL-DBS) techniques for reducing hand tremors in many disorders, notably Parkinson's patients, in recent years. As shown in the studies, hand tremor and rigidity symptoms are two sides of the same coin, with hand tremor leading to rigidity fluctuations and vice versa. The following are the primary contributions of this study: 1) depicts the correlation between the two indicators and 2) the suggestion of a proximal policy optimization (PPO)-based non-integer proportional integral derivative (PID) control technique to reduce hand tremor and stiffness at the same time, where the reward function is developed to alter DBS settings when illness levels vary. The PPO technique is combined with the main controller to provide adaptive learning. The controller parameters of the non-integer PID scheme are regarded as changeable controller coefficients in the proposed strategy, which will be adaptively built by the PPO technique via online learning of its neural networks (NNs). In order to demonstrate the advantages and adaptability of the procedure with different models and patients, the recommended strategy is an analysis by computer simulation in a variety of contexts (performance, robustness, and noise) and compared to current methodologies like integer-order PID (IOPID) and fractional-order PID (FOPID). The simulation outcomes showed that the created system performed better and more effectively than previous approaches in coping with parameter changes and outside noise, in addition to simultaneously reducing hand tremor and stiffness.