Dynamic synchronization state identification

Conference item

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.

Authors: Luo, H; Du, X; Huang, Y; Green, A; Aziz, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Host title: 8th International IEEE/EMBS Conference on Neural Engineering (NER 2017)
• Journal: 8th International IEEE/EMBS Conference on Neural Engineering (NER 2017)
• Publication date: 2017-08-15
• Acceptance date: 2017-02-06
• DOI: 10.1109/NER.2017.8008405
• ISSN: 1948-3554, 1948-3546
• ISBN: 9781538619162