Algorithmic Complexity of EEG for Prognosis of Neurodegeneration in Idiopathic Rapid Eye Movement Behavior Disorder (RBD)

Abstract

Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson’s disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We 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 ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel–Ziv–Welch compression. Complexity 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. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic 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.

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Acknowledgments

This work has been partly supported by the Michael J. Fox Foundation project “Discovery of EEG biomarkers for Parkinson Disease and Lewy Body Dementia using advanced Machine Learning techniques” (Rapid Response Innovation Awards 2013), the FET Open Luminous project (H2020-FETOPEN-2014- 2015-RIA under agreement No. 686764) as part of the European Union’s Horizon 2020 research and training programme 2014-2018, the Canadian Institutes of Health Research (CIHR) and the W. Garfield Weston Foundation.

Disclosure

Starlab and Neuroelectrics authors have an interest in developing commercial, translational applications from this research. GR is a shareholder of both companies.

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Contributions

DI and MC pre-processed EEG data to produce artifact free spectrograms. EK and ASF contributed to code development and revised the manuscript. JFG, RP and JM collected the EEG data and revised the manuscript. GR provided the complexity analysis code, carried out the complexity and statistical analysis, and wrote the manuscript.

Corresponding author

Correspondence to Giulio Ruffini.

Additional information

Starlab Barcelona, http://starlab.es.

Associate Editor Xiaoxiang Zheng oversaw the review of this article.

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Ruffini, G., Ibañez, D., Kroupi, E. et al. Algorithmic Complexity of EEG for Prognosis of Neurodegeneration in Idiopathic Rapid Eye Movement Behavior Disorder (RBD). Ann Biomed Eng 47, 282–296 (2019). https://doi.org/10.1007/s10439-018-02112-0

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Keywords

  • Parkinson’s disease
  • Complexity
  • Algorithmic complexity
  • LZW
  • Lempel–Ziv–Welch compression
  • RBD
  • Time-frequency analysis
  • Dementia with Lewy bodies
  • DLB