Dynamic synchronization state identification

Huichun Luo, Xueying Du, Yongzhi Huang, A. Green, T. Aziz, Shouyan Wang
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引用次数: 1

Abstract

In the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) nucleus, the synchronization level of multiple frequency band oscillations of local field potentials (LFPs) have been shown to be associated with chronic pain perception and modulation. In this study, a state identification approach was generated to dynamically identify the synchronization state of neural oscillation. In this approach, a pattern extraction model was created to characterize the patterning of the neural oscillations based on wavelet packet transform. The value of wavelet packet coefficients represents the synchronization level of pattern. And then a state discrimination model was designed to distinguish the synchronization state and de-synchronization state of pattern based on calculating a suitable threshold and discrimination strategies. By using the sensory thalamus and PVAG LFPs of neuropathic pain and simulation signals, the parameters of the approach were optimized for theta pattern (6–9Hz) and alpha pattern (9–12hz) identification respectively. Finally, the mean best performance of identifying the theta pattern states from 300s simulation signals achieved 91% sensitivity and 86% specificity, and achieved 80% sensitivity and 88% specificity for alpha pattern state identification. Then this approach was applied to the sensory thalamus and PVAG LFPs, and was able to identify the synchronization state of theta and alpha pattern. This study provides a reliable approach to dynamically identify the synchronization level of pattern of neuropathic pain disease through optimizing the parameters. Based on this approach, a real-time monitoring of the pain state and an adaptive treatment regimen can be achieved.
动态同步状态识别
在感觉丘脑和脑室周围灰核/导水管周围灰核(PVAG)中,局部场电位(LFPs)多频段振荡的同步水平与慢性疼痛感知和调节有关。本文提出了一种状态识别方法来动态识别神经振荡的同步状态。在该方法中,基于小波包变换建立了一种模式提取模型来表征神经振荡的模式特征。小波包系数的大小表示模式的同步程度。在此基础上,设计了一种状态判别模型,通过计算合适的阈值和判别策略来判别模式的同步状态和去同步状态。利用神经性疼痛的感觉丘脑和PVAG lfp和模拟信号,分别优化了该方法的参数,以识别6-9Hz的θ模式和9-12hz的α模式。最后,从300个模拟信号中识别theta模式状态的平均最佳性能达到91%的灵敏度和86%的特异性,alpha模式状态识别的平均最佳性能达到80%的灵敏度和88%的特异性。然后将该方法应用于感觉丘脑和PVAG LFPs,并能够识别theta和alpha模式的同步状态。本研究通过优化参数,为动态识别神经性疼痛疾病模式同步水平提供了可靠的方法。基于这种方法,可以实现对疼痛状态的实时监测和适应性治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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