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Measuring Real-Time Medication Effects From Electroencephalography

37 Pages Posted: 7 Dec 2020

See all articles by Aarti Sathyanarayana

Aarti Sathyanarayana

Harvard University - Computational Health Informatics Program

Rima El Atrache

Harvard University - Department of Neurology

Michele Jackson

Harvard University - Department of Biostatistics; Boston Children’s Hospital - Department of Neurology

Sarah Cantley

Harvard University - Department of Biostatistics

Latania Reece

Harvard University - Department of Biostatistics

Claire Ufongene

Harvard University - Department of Biostatistics

Tobias Loddenkemper

Harvard University - Computational Health Informatics Program

Kenneth Mandl

Boston Children's Hospital - Computational Health Informatics Program

William Bosl

Harvard University - Computational Health Informatics Program

More...

Abstract

Background: Evaluating the effects of anti-seizure medication and response to treatment in patients with epilepsy remains a slow, challenging and manual process. A digital biomarker for medication effects and/or epileptogenicity would provide useful and objective information that guides clinical decision making. The aim of this study is to determine if the effect of anti-seizure medications on patients with epilepsy can be quantitatively measured in near real-time from electroencephalography (EEGs).

Methods: We screened patients admitted to the Long-Term Monitoring Unit at Boston Children’s Hospital for pre-surgical evaluation.  We selected two 30-second EEG excerpts from a period before and after medication weaning, were selected for nonlinear analysis from each patient. 

Findings: We found measurable effects of anti-seizure medications across all nonlinear measures of patients on high or low amounts on medication.  Patients with a moderate number of expected seizures per day showed a larger medication effect on the brain than patients with rare or frequent seizures. Patients on four or more medications showed the largest effect size pre- and post- medication weaning. The seizure onset zone of the brain responded differently to medication than the rest of the brain. Patients with a clinically-determined lack of response to medication had distinctly different brain electrodynamics compared to patients who did have a response to medication. These medication effects correlate with the inferred level of epileptogenicity in a patient’s brain.

Interpretation: Multifrequency nonlinear EEG analysis shows promise for identifying digital biomarkers to measure medication effects and evaluate response to treatment in patients with epilepsy.

Funding Statement: Dr. Sathyanarayana was supported by T32HD040128 from the NICHD/NIH. TL, REA, and MJ were supported by the Epilepsy Research Fund.

Declaration of Interests: WJB and TL are named on a patent submitted and held by the Boston Children’s Hospital Technology Development Office that includes the signal analysis methods discussed in this article. TL is part of patent applications to detect and predict clinical outcomes, and to manage, diagnose, and treat neurological conditions, epilepsy, and seizures. The other authors declare that they have no other competing financial or non-financial interests.

Ethics Approval Statement: The Institutional Review Board approved the study prior to data acquisition, and was deemed exempt from consent.

Keywords: Epilepsy, EEG, neurophysiology, nonlinear dynamics, biomarkers

Suggested Citation

Sathyanarayana, Aarti and Atrache, Rima El and Jackson, Michele and Cantley, Sarah and Reece, Latania and Ufongene, Claire and Loddenkemper, Tobias and Mandl, Kenneth and Bosl, William, Measuring Real-Time Medication Effects From Electroencephalography. Available at SSRN: https://ssrn.com/abstract=3734276 or http://dx.doi.org/10.2139/ssrn.3734276

Aarti Sathyanarayana

Harvard University - Computational Health Informatics Program

United States

Rima El Atrache

Harvard University - Department of Neurology ( email )

300 Longwood Avenue
Boston, MA 02115
United States

Michele Jackson

Harvard University - Department of Biostatistics

Boston, MA
United States

Boston Children’s Hospital - Department of Neurology

300 Longwood Avenue
Boston, MA 02115
United States

Sarah Cantley

Harvard University - Department of Biostatistics ( email )

Boston, MA
United States

Latania Reece

Harvard University - Department of Biostatistics ( email )

Boston, MA
United States

Claire Ufongene

Harvard University - Department of Biostatistics ( email )

Boston, MA
United States

Tobias Loddenkemper

Harvard University - Computational Health Informatics Program

United States

Kenneth Mandl

Boston Children's Hospital - Computational Health Informatics Program ( email )

300 Longwood Avenue
Boston, MA 02115
United States

William Bosl (Contact Author)

Harvard University - Computational Health Informatics Program ( email )

United States

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