In this paper, a new particle swarm optimization particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with mutation operator, and is used for real-time filtering and noise reduction of nonlinear vibration signals. Because of its introduction of mutation operator, this algorithm overcomes the problem where by particle swarm optimization (PSO) algorithm easily falls into local optimal value, with a low calculation accuracy. At the same time, the distribution and diversity of particles in the sampling process are improved through the mutation operation. The defect of particle filter (PF) algorithm where the particles are poor and the utilization rate is not high is also solved. The mutation control function makes the particle set optimization process happen in the early and late stages, and improves the convergence speed of the particle set, which greatly reduces the running time of the whole algorithm. Simulation experiments show that compared with PF and PSO-PF algorithms, the proposed NPSO-PF algorithm has lower root mean square error, shorter running time, higher signal-to-noise ratio and more stable filtering performance. It is proved that the algorithm is suitable for real-time filtering and noise reduction processing of nonlinear signals.

Objective: Freezing of gait (FOG) is a common disabling motor symptom in Parkinson's disease (PD), but the potential pathogenic mechanisms are still unclear. Methods: A total of 22 patients with PD with FOG (PD-FOG), 28 patients with PD without FOG (PD-nFOG), and 33 healthy controls (HCs) were recruited in this study. Degree centrality (DC)-a graph theory-based measurement of global connectivity at the voxel level by measuring the number of instantaneous functional connections between one region and the rest of the brain-can map brain hubs with high sensitivity, specificity, and reproducibility. DC was used to explore alterations in the centrality of PD-FOG correlated with brain node levels. PD-FOG cognitive network dysfunction was further revealed via a seed-based functional connectivity (FC) analysis. In addition, correlation analyses were carried out between clinical symptoms and acquired connectivity measurement. Results: Compared to the PD-nFOG group, the PD-FOG group showed remarkably increased DC values in the right middle frontal gyrus (RMFG). There were no significant differences in other gray matter regions. Importantly, the clinical severity of FOG was related to the mean DC values in the RMFG. This brain region served as a seed in secondary seed-based FC analysis, and we further found FC changes in the right precuneus, right inferior frontal gyrus, right superior frontal gyrus (SFG), and cerebellum. Conclusion: Increased RMFG activity and FC network alterations in the middle frontal cortex with the precuneus, inferior, and SFG, and the cerebellum may have great potential in brain dysfunction in PD with FOG.

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