Research & Publications
The research presented here, funded by the European Union Framework Program for Research and Innovation Horizon 2020, Marie Sklodowska-Curie Grand Agreement No. 99791, addresses real-time processing of brain biosignals applied to epileptiform discharges, behavior, and cognition, and is called Digital Response Test in Epilepsy (DigRTEpi).
Extensive Research Description
The DigRTEpi project focuses on persons with epilepsy, testing epileptiform phenomena that occur between epileptic seizures and can temporarily and often severely affect their activities of daily living. These epileptiform phenomena are called interictal epileptiform discharges (IEDs), which are short electrical disturbances of brain function that are usually not self-perceived by patients, cannot be detected by normal observation, but can occur much more frequently than epileptic seizures. IEDs can be recorded in an electroencephalogram (EEG), and their effects on behavior and cognition, also known as transient cognitive impairment, can be visualized with appropriate testing. IEDs can slow reaction times, but can also be associated with failure to perform a task, depending on the duration and other electrophysiological characteristics of the bursts. The goal is to identify and predict the "dangerous" IEDs, e.g., regarding the fitness-to-drive, but there are no standardized tests to study them. A second problem is their short duration, which requires rapid detection and close temporal correlation with a test task to measure their effects. Therefore, procedures that visualize the effects of IEDs and provide the opportunity for standardized clinical practice testing are important: they could contribute to treatment adaptation and disease coping, thereby improving quality of life and socioeconomic outcomes.
We present four technical innovations: To the best of our knowledge, the first algorithm that routinely detects surface electroencephalogram (EEG)-derived IEDs in real time. Second, a solution to the problem that synchronization of video EEG data recorded with proprietary software is often lost when the data is exported and opened with other programs. Third, a technique for measuring true human reaction times to visual stimuli, independent of the digital latency of computers, networks, and monitors connected in series. Fourth, a standardized and international method to analyze transient cognitive impairment during brief IEDs, the interictal automated responsiveness test (iART), based on Automated Responsiveness Testing in Epilepsy (ARTiE), a standardized analysis of behavior during epileptic seizures (Blumenfeld lab, Yale). These innovations will be tested in 2022 in a patient pilot study with two study arms at Yale New Haven Hospital, USA, and at the Epilepsy Center Frankfurt Rhein-Main, Germany (see clinical trials).
Publications, topic-related since 2019
- Krestel H, Rosenow F, Blumenfeld H, von Allmen A. Real-time EEG classification with convolutional networks and ResNet. Conference proceeding American Epilepsy Society meeting 2019; https://cms.aesnet.org/abstractslisting/real-time-eeg-classification-with-convolutional-networks-and-resnet
- DOI: 10.1111/epi.16356 Realistic driving simulation during generalized epileptiform discharges to identify electroencephalographic features related to motor vehicle safety: Feasibility and pilot study. Epilepsia 2020;61:19-28;
- Markhus R, Henning O, Molteberg E, et al. EEG in fitness to drive evaluation in people with epilepsy - variations across Europe. Seizure: European Journal of Epilepsy 2020;79:56-60. DOI: 10.1016/j.seizure.2020.04.013
- Kumar A, Martin R, Chen W, et al. Simulated driving in the epilepsy monitoring unit: Effects of seizure type, consciousness, and motor impairment. Epilepsia 2022;63:e30-e34. DOI: 10.1111/epi.17136
- Kumar A, Martin R, Chen W, et al. Simulated driving in the epilepsy monitoring unit: Effects of seizure type, consciousness, and motor impairment. Erratum. Epilepsia. 2022 Feb 25. DOI: 10.1111/epi.17203
- Abukhadra Y, Li J, Springer M, Khalaf A, Roethlisberger S, Krestel H, Blumenfeld H. EEG and Machine Learning in Prediction of Impaired Responses to Visual Stimuli During Interictal Epileptiform Discharges. Conference proceeding American Epilepsy Society meeting 2021; https://cms.aesnet.org/abstractslisting/eeg-and-machine-learning-in-prediction-of-impaired-responses-to-visual-stimuli-during-interictal-epileptiform-discharges
- Krestel H, Schreier D, Sakiri E, et al. Predictive power of interictal epileptiform discharges in fitness-to-drive evaluation, in revision.
Adolescent; Adult; Diagnostic Techniques, Neurological; Electroencephalography; Epilepsies, Partial; Epilepsy, Generalized; Genetics, Medical; Patient Safety; Drug Resistant Epilepsy
Public Health Interests
Behavioral Health; Chronic Diseases; Clinical Trials; Genetics, Genomics, Epigenetics