Algorithmic complexity of EEG for prognosis of neurodegeneration in idiopathic rapid eye movement behavior disorder (RBD)

ObjectiveIdiopathic REM sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinsons disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis.\n\nMethodsWe analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from[~] 5 minute recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression (LZW).\n\nResultsComplexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels.\n\nConclusionsPoor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude coupling.\n\nSignificanceAlgorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.\n\nIndex TermsBiomarkers, EEG, LZW, PD, LBD