Computational characteristics of interictal EEG as objective markers of epileptic spasms

Rachel J Smith; Derek K Hu; Daniel W Shrey; Rajsekar Rajaraman; Shaun A Hussain; Beth A Lopour

doi:10.1016/j.eplepsyres.2021.106704

Computational characteristics of interictal EEG as objective markers of epileptic spasms

Epilepsy Res. 2021 Oct:176:106704. doi: 10.1016/j.eplepsyres.2021.106704. Epub 2021 Jun 25.

Authors

Rachel J Smith¹, Derek K Hu¹, Daniel W Shrey², Rajsekar Rajaraman³, Shaun A Hussain³, Beth A Lopour⁴

Affiliations

¹ Department of Biomedical Engineering, University of California, Irvine, CA, United States.
² Division of Neurology, Children's Hospital of Orange County, Orange, CA, United States; Department of Pediatrics, University of California, Irvine, CA, United States.
³ Division of Pediatric Neurology, University of California, Los Angeles, CA, United States.
⁴ Department of Biomedical Engineering, University of California, Irvine, CA, United States. Electronic address: beth.lopour@uci.edu.

PMID: 34218209
DOI: 10.1016/j.eplepsyres.2021.106704

Abstract

Objective: Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES.

Methods: We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient.

Results: In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96).

Conclusions: Computational EEG features successfully distinguish ES patients from controls in a large, blinded study.

Significance: These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.

Keywords: Detrended fluctuation analysis; Entropy; Functional connectivity; Infantile spasms; Long-range temporal correlations; West syndrome.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electroencephalography / methods
Humans
Sleep
Spasm
Spasms, Infantile* / drug therapy
Wakefulness