Optimization of spectral analysis of electrophysiological recordings of the subthalamic nucleus in Parkinson’s disease: A retrospective study

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder that is diagnosed in over 60.000 people per year in the USA alone. Deep brain stimulation of the STN has been implemented to ameliorate the motor symptoms, being highly effective. PD has been associated with increased power in the beta frequency band (13-35 Hz) in the STN’s stereo electroencephalography signals (sEEG). Several studies have estimated the spectrum of the sEEG signals in order to identify spectral behavior according to anatomical structures and pathologies; however, the estimation methods do not give enough sensitivity. In the present study, we hypothesize that parametric methods can have a better performance to correctly estimate the spectrum of a sEEG signal in the beta band using the right model order. AR models were estimated, with four different information criteria to choose the proper order, where the Akaike’s information criterion corrected (AICc) gives the best estimation with an order of 17.