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Computational EEG attributes predict response to therapy for epileptic spasms.
- Source :
-
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology [Clin Neurophysiol] 2024 Jul; Vol. 163, pp. 39-46. Date of Electronic Publication: 2024 Apr 10. - Publication Year :
- 2024
-
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.<br />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.<br />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).<br />Conclusion: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse.<br />Significance: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.<br /> (Copyright © 2024. Published by Elsevier B.V.)
Details
- Language :
- English
- ISSN :
- 1872-8952
- Volume :
- 163
- Database :
- MEDLINE
- Journal :
- Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
- Publication Type :
- Academic Journal
- Accession number :
- 38703698
- Full Text :
- https://doi.org/10.1016/j.clinph.2024.03.035