Computational EEG attributes predict response to therapy for epileptic spasms

Rajsekar R Rajaraman; Rachel J Smith; Shingo Oana; Atsuro Daida; Daniel W Shrey; Hiroki Nariai; Beth A Lopour; Shaun A Hussain

doi:10.1016/j.clinph.2024.03.035

Computational EEG attributes predict response to therapy for epileptic spasms

Clin Neurophysiol. 2024 Jul:163:39-46. doi: 10.1016/j.clinph.2024.03.035. Epub 2024 Apr 10.

Authors

Rajsekar R Rajaraman¹, Rachel J Smith², Shingo Oana¹, Atsuro Daida¹, Daniel W Shrey³, Hiroki Nariai¹, Beth A Lopour⁴, Shaun A Hussain⁵

Affiliations

¹ Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
² Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA.
³ Division of Pediatric Neurology, University of California, Irvine, Irvine, CA, USA; Department of Neurology, Children's Hospital of Orange County, Orange, CA, USA.
⁴ Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
⁵ Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: shussain@mednet.ucla.edu.

PMID: 38703698
DOI: 10.1016/j.clinph.2024.03.035

Abstract

Objective: We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes.

Methods: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively.

Results: After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044).

Conclusion: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse.

Significance: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.

Keywords: Entropy; Functional connectivity; Hypsarrhythmia; Long-range temporal correlations; West syndrome.

MeSH terms

Anticonvulsants / therapeutic use
Child
Child, Preschool
Electroencephalography* / methods
Female
Humans
Infant
Male
Predictive Value of Tests
Spasms, Infantile* / diagnosis
Spasms, Infantile* / drug therapy
Spasms, Infantile* / physiopathology
Spasms, Infantile* / therapy
Treatment Outcome

Substances

Anticonvulsants