Dynamic synchronization state discrimination in local field potentials of neuropathic pain

Local field potentials (LFPs) contain rich information of deep brain nucleus. And the dynamic multiple oscillations' synchronization activity of deep brain structures are involved in the neurophysiological and neuropathological function of a nucleus. In this study, a state identification approach was generated for identify the synchronization state of theta (6-9 Hz) and alpha (9-12 Hz) neural oscillations in neuropathic pain. The state identification approach consists oscillation extraction model and state discrimination model based on wavelet packet transform and adaptive thresholding strategy. The value of wavelet packet coefficients represented the synchronization state of theta and alpha oscillation. Then the coefficients were compared to the threshold to determine the state of oscillation according to the determination strategy. The parameters of this approach were optimized with 20 sides the sensory thalamus and periventricular gray/ periaqueductal gray (PVAG) LFPs of neuropathic pain and simulation signals. The best performance of theta oscillation state identification in -9 dB simulation signal achieved 85% sensitivity and 85% specificity. The best performance of alpha oscillation state identification in -11 dB simulation signal achieved 77% sensitivity and 85% specificity. Finally, the state identification approach was apply to identify theta and alpha oscillation state for neuropathic pain and got total 6 combination states. This study provides an approach to reliably discriminate the synchronization state of oscillations in neural signals. Based on this approach, a real-time monitoring of the pain neural state and an adaptive treatment regimen can be achieved.