Particle Swarm Optimization Algorithm to Optimize the Activity Patterns of Internal Globus Pallidus in Parkinson Disease
Shri Dhar1, Sanjay Yadav2, Jyotsna Singh3, AK Yadav4, Phool Singh5
1Shri Dhar, Department of Applied Sciences, The North Cap University Gurugram, Haryana, India.
2Sanjay Yadav*, Department of Applied Sciences, The North Cap University Gurugram, Haryana, India.
3Jyotsna Singh, IILM Academy of Higher Learning, College of Engineering and Technology, Greater Noida, Uttar Pradesh, India.
4AK Yadav, Amity School of Applied Sciences, Amity University Haryana, Gurugram, India.
5Phool Singh, Department of Mathematics, SoET, Central University of Haryana, Mahendergarh, Haryana, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 5382-5389 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6923018520 /2020©BEIESP | DOI: 10.35940/ijrte.E6923.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Parkinson disease, one of the widely known brain disorders, affects the movement of body parts due to dopamine reduction in the basal ganglia. In this paper, a model of internal globuspallidus is taken into consideration for a primate suffering from Parkinson disease. The discharge patterns generated for a primate suffering from Parkinson disease are compared with the discharge patterns of a healthy primate. The lags in the discharge patterns of primate with Parkinson disease due to slow movement are shown in the result section of the paper. On the basis of analysis of the model, four parameters have been optimized to remove these lags; which are membrane potential from GPe to GPi (denoted by ), membrane potential from STN to GPi (denoted by ), synaptic conductance from GPe to GPi (denoted by ) and synaptic conductance from STN to GPi (denoted by ) as the model is most sensitive to these four parameters among all the parameters taken into consideration. Optimization of these parameters has been carried out using particle swarm optimization algorithm over a time span of 300 msec. A qualitative comparison has been made using correlation coefficient computed between the discharge patterns for a healthy primate and primate with Parkinson disease. The value of correlation coefficient turns out to be 0.9994 showing very high degree of overlap between the two discharge patterns, and validates the high degree of accuracy of the results obtained by particle swarm optimization algorithm.
Keywords: Activity pattern, Internal globuspallidus, Parkinson disease, particle swarm optimization algorithm.
Scope of the Article: Design Optimization of Structures