A Digital Biomarker for Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS)

Abstract

Background: Benign childhood epilepsy with centrotemporal spikes (BECTS), also known as Rolandic Epilepsy, is one of the most common forms of focal childhood epilepsy. Despite its prevalence, BECTS is challenging to diagnose because of the nocturnal and brief nature of the seizures. While most epilepsy research has focused on early warning signs of seizure onset, a separate challenge is to detect functional characteristics - biomarkers - that indicate the brain has entered a dynamical state in which seizures are more likely to occur. This has the potential to more reliably evaluate the underlying neurophysiology associated with BECTS and potentially predict clinical outcomes, and improve diagnostic screening for BECTS. Methods: Electronic medical records were retrospectively reviewed to identify patients with BECTs diagnosis codes admitted to the long-term monitoring unit at Boston Children's Hospital (BCH). Thirty one patients with BECTS (13 female, median age 9.5 years) and thirty-one control patients (median age 10 years, 10 female) were selected for inclusion in this study. EEGs were reviewed to confirm the BECTS diagnosis. EEG segments that lacked epileptiform activity were selected for analysis from awake BECTS patients and controls. Multiscale nonlinear methods were used to computed dynamical values from the EEG segments. Findings: Two related findings are reported in this study. First, several nonlinear measures derived from EEGs are clearly different in the centrotemporal region of BECTS patients when compared to the same region in controls. Because all results are from awake patients, this suggests a possible biomarker for BECTS in an awake patient. Secondly, these measures clearly distinguish the centrotemporal and extra-centrotemporal regions in the same patient, suggesting that these nonlinear measures may tentatively be useful for detecting the epileptogenic zone. Interpretation: The results presented in this study suggest that nonlinear features computed from EEG signals may be useful for identifying BECTS in awake patients, introducing a digital biomarker with the potential to improve diagnostic screening for BECTS and predict clinical outcomes. The finding that the centrotemporal region appears to be dynamically different from the rest of the brain in BECTS patients suggests the possibility that the nonlinear methods used in this study may be useful for identifying the irritative or epileptic zone. Funding Statement: Dr. Sathyanarayana was supported by T32HD040128 from the NICHD/NIH. Dr. Loddenkemper, Dr. El Atrache and Ms. Jackson 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 authors declare that they have no other competing financial or nonfinancial interests. Ethics Approval Statement: Approval from the Boston Children’s Hospital Institutional Review Board was obtained prior to data acquisition.