ORCID Profile
0000-0002-1820-8024
Current Organisations
Central University of Haryana
,
Malaviya National Institute of Technology
,
York University
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Publisher: Wiley
Date: 24-11-2022
DOI: 10.1002/MDS.29276
Abstract: Local field potentials (LFPs) represent the summation of periodic (oscillations) and aperiodic (fractal) signals. Although previous studies showed changes in beta band oscillations and burst characteristics of the subthalamic nucleus (STN) in Parkinson's disease (PD), how aperiodic activity in the STN is related to PD pathophysiology is unknown. The study aimed to characterize the long‐term effects of STN–deep brain stimulation (DBS) and dopaminergic medications on aperiodic activities and beta bursts. A total of 10 patients with PD participated in this longitudinal study. Simultaneous bilateral STN‐LFP recordings were conducted in six separate visits during a period of 18 months using the Activa PC + S device in the off and on dopaminergic medication states. We used irregular‐res ling auto‐spectral analysis to separate oscillations and aperiodic components (exponent and offset) in the power spectrum of STN‐LFP signals in beta band. Our results revealed a systematic increase in both the exponent and the offset of the aperiodic spectrum over 18 months following the DBS implantation, independent of the dopaminergic medication state of patients with PD. In contrast, beta burst durations and litudes were stable over time and were suppressed by dopaminergic medications. These findings indicate that oscillations and aperiodic activities reflect at least partially distinct yet complementary neural mechanisms, which should be considered in the design of robust biomarkers to optimize adaptive DBS. Given the link between increased gamma‐aminobutyric acidergic (GABAergic) transmission and higher aperiodic activity, our findings suggest that long‐term STN‐DBS may relate to increased inhibition in the basal ganglia. © 2022 International Parkinson and Movement Disorder Society.
Publisher: MDPI AG
Date: 15-06-2022
DOI: 10.3390/MATH10122076
Abstract: Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance.
Publisher: MDPI AG
Date: 22-04-2022
DOI: 10.3390/MATH10091411
Abstract: Data mining applications are growing with the availability of large data sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.
Publisher: Elsevier BV
Date: 05-2020
Publisher: Informa UK Limited
Date: 02-05-2023
Location: India
No related grants have been discovered for Akash saxena.