Phase slope transfer entropy analysis of EEG in patients with Parkinson's disease

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by the loss of dopaminergic neurons, leading to motor and non-motor symptoms that severely impact patients' life. This research combines the advantages of slope symbolization and phase transfer entropy (PTE) to propose the phase slope transfer entropy (PSlope-TE) algorithm. Simulation experiments verified that PSlope-TE outperformed baseline algorithms in terms of stability under varying data lengths and robustness against noise interference. Applied to electroencephalogram (EEG) signals, PSlope-TE revealed that PD patients in the OFF state exhibited stronger information transfer in the delta, theta, and gamma frequency bands compared to healthy controls(HC), particularly in the frontal motor regions. In contrast, differences between HC and PD patients in the ON state were less pronounced. Overall, the proposed algorithm assists in comprehending the effects of PD and Levodopa therapy on brain information transmission and coupling dynamics.