Abstract :
Parkinson’s disease (PD) is one of the most commonest neurodegenerative chronic movement disorders, is caused by damage to the central nervous system (CNS). The manifestations or symptoms analogousto cardinal motoric features of PD have been mentioned as ‘Kampavata‘ in ancient Sanskrit Vedic Hindi documents. Parkinson‘s disease was termed “Shaking Palsy” by the Galen, a famed Roman physician. Irrespective of all the studies on PD, the formation mechanism of its symptoms remained unknown. It is still not obvious why damage only to the substantia nigra pars compacta, a small part of the brain, causes a wide range of symptoms. Moreover, the causes of brain damages remain to be fully elucidated. Exact understanding of the brain function seems to be impossible. Equally, various engineering and technological software tools are challenging to understand the behavior and performance of complex convoluted systems. Computational models are the most significant tools in this connection. Developing computational models and analysis for the PD has begun in recent decades which are effective not just in understanding the disease but contributing new therapies, and its prediction and control, and also in its early diagnosis. Modeling studies include two main groups: black-box models and gray-box models. Generally, in the blackbox modeling, regardless of the system information, the symptom is only considered as the output. Such models, besides the computational analysis studies, increase our knowledge of the disorders behavior and the disease symptoms. The gray-box models consider the involved structures in the symptoms appearance as well as the final disease symptoms. These models can effectively save time and be cost-effective for the researchers and help them select appropriate treatment mechanisms among all possible options. In
this study (survey/review paper), primary efforts are made to investigate some studies on Parkinson‘s disease and computational analysis. Then, computational analysis of microelectrode recordings (MER) of subthalamic nucleus (STN) neural signal acquisition of Parkinson‘s deep brain stimulation (DBS), i.e., MER with STN-DBS with a machine learning (ML) approach using clustering and principal component based targeting method followed by novel algorithms will be evaluated. Finally, the results of using such methods are presented significantly as a preliminary report. With the advent of high-speed powerful computing machines and artificial intelligence based machine learning techniques, the researchers are fully utilized these analyses for predicting and detecting early symptoms and signs of PD and for extracting its feature manifestations (tremor, Bradykinesia, postural, and postural instability).
Keyword :
Behavior simulation, Brain disorders, Cluster Analysis, Data Reduction, Decomposition (PCA), Deep Brain Stimulator (DBS), Feature extraction/selection, KLTransform, Machine learning (ML), Mathematical analysis, Microrecording (MER), Parkinson‘s disease (