Heinz Eric Krestel, MD

People with epilepsy are not only affected by seizures but also by epileptiform phenomena that occur between seizures, known as interictal epileptiform discharges (IEDs). IEDs are typically not perceived by patients nor recognizable by routine clinical observation. Nevertheless, IEDs can have serious health and societal consequences and their much higher prevalence compared to seizures. IED-induced deficits can affect every activity of daily life, depending on whether the corresponding brain region is affected, and severity can range from negligible impairment to the inability to perform an action with serious consequences, such as overlooking a stop sign and causing an accident. There are experimental tests to visualize IED-induced deficits, but there are no standardized tests that can be routinely employed to detect IED-associated effects on daily social functioning, for example early after the onset of the epilepsy, or to test the fitness-to-drive. Because epilepsy is one of the most common neurological disorders with a prevalence of 1%, there is a societal and economic need for a user-friendly, inexpensive, and standardized test to objectively measure IED-associated effects and thus the risk of not being able to respond appropriately, and to provide these measurements to health-care personnel for consultation of people with epilepsy who are seizure-free at the time of examination. We have developed a Digital Response Test in Epilepsy (DigRTEpi) in a multi-national collaboration of the Epilepsy Center Frankfurt Rhine-Main (Germany), Von Allmen Engineering (Switzerland), and the Yale School of Medicine (USA), under the leadership of the lead inventor, and funded by the European Union Framework Program for Research and Innovation Horizon 2020, Marie Sklodowska-Curie Grand Agreement No. 99791. DigRTEpi measures IED-bursts (≥ 1 epileptiform potential of a least 0.4 seconds and up to several seconds in duration) and automatically analyzes IED-burst effects in real time using a test for simulated driving and a neuropsychological bedside test using a laptop computer. Thus, the risk of IED-induced deficits in people with epilepsy can be assessed in an objective and standardized manner in two social domains, namely road traffic and everyday communicative skills. The artificial intelligence used in DigRTEpi consists of an advanced visualization technique that calculates the new information content, or entropy, of a window that progressively slides along a recording of electrical brain waves (electroencephalogram, or EEG). A deep neuronal network classifies each window’s content in real-time and triggers either an obstacle in the driving videogame or a video in a computer-based neuropsychological test. The significance of DigRTEpi is that it can give broad access to best testing of IED effects because it is objective, user-friendly, portable, and compatible with various commercial EEG recording devices. It will contribute to defining the criteria of an epileptiform spike or IED-burst with clinical relevance to daily social functioning of the awake epilepsy patient. DigRTEpi could be used to improve disease coping by people with epilepsy themselves by informing them about the prevalence, consequences, and risks of IEDs. DigREpi should be employed in clinical routine to screen for IED-induced neuropsychological deficits, clarify the need for treatment optimization and social reintegration support of children and adolescents newly diagnosed with epilepsy, thereby improving educational and vocational integration, and thus quality of life. DigRTEpi can also improve road safety and help focus resources for fitness-to-drive evaluation by enabling the assessment of many medical findings and questions that today remain unanswered by conventional methods, thereby reducing the number of people with epilepsy who need to be tested in a second instance. Finally, both the artificial intelligence and the medical electronics of DigRTEpi can be used and modified by companies that work, for example, on brain-computer interfaces and neurofeedback, or in the car industry using driver assistance systems, because the artificial intelligence used by DigRTEpi is currently the fastest method applying deep learning in science and in the market.

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
• Cohen E, Antwi P, Banz BC, Vincent P, Saha R, Arencibia CA, Ryu JH, Atac E, Saleem N, Tomatsu S, Swift K, Hu C, Krestel H, Farooque P, Levy S, Wu J, Crowley M, Vaca FE, Blumenfeld H. 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; DOI: 10.1111/epi.16356
• Markhus R, Henning O, Molteberg E, Hećimović H, Ujvari A, Hirsch E, Rheims S, Surges R, Malmgren K, Rüegg S, Gil-Nagel A, Roivainen R, Picard F, Steinhoff B, Marusic P, Mostacci B, Kimiskidis VK, Mindruta I, Jagella C, Mameniškienė R, Schulze-Bonhage A, Rosenow F, Kelemen A, Fabo D, Walker MC, Seeck M, Krämer G, Arsene OT, Krestel H, Lossius M. 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, Bauerschmidt A, Youngblood MW, Cunningham C, Si Y, Ezeani C, Kratochvil Z, Bronen J, Thomson J, Riordan K, Yoo JY, Shirka R, Manganas L, Krestel H, Hirsch LJ, Blumenfeld H 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
• Springer M, Khalaf A, Vincent P, Ryu JH, Abukhadra Y, Beniczky S, Glauser T, Krestel H, Blumenfeld H. A Machine Learning Approach for Predicting Impaired Consciousness in Absence Epilepsy. Annals of Clinical and Translational Neurology 2022. DOI: https://doi.org/10.1002/acn3.51647
• Krestel H, Rackauskaite J, Khoueiry M, von Allmen A, Li YY, Pereira Marcal G, Quiroz A, Erceg N, Panos L, Pelczar M, Schoretsanitis G, Cicek M, Zedka M, Jagella C, Markhus C, Rosenow F, Blumenfeld H. The interictal automated responsiveness test (iART) analyzes transient cognitive impairment in an international manner. Conference proceeding European Epilepsy Congress 2022; https://eec2022.abstractserver.com/program/#/details/presentations/1120
• European Epilepsy Congress 2022 Forum, Title: "Real-time processing of brain biosignals; applied to epileptiform discharges, behavior, and cognition" Chair: Heinz Krestel (Switzerland) State of the art of machine learning algorithms for real-time biosignal processing, advantages, disadvantages, potential applications - Speaker: Diyuan Lu (Germany);Brain-computer interfaces and its latencies - Speaker: Heinz Krestel (Switzerland);Detection of transient cognitive impairment in dementia evaluation - Speaker: Justina Rackauskaite (Switzerland); Detection of transient cognitive impairment in fitness-to-drive evaluation - Speaker: Heinz Krestel (Switzerland)
• Krestel H, Schreier D, Sakiri E, von Allmen A, Abukhadra Y, Nirkko A, Steinlin M, Rosenow F, Markhus R, Jagella C, Mathis J, Blumenfeld H. Predictive power of interictal epileptiform discharges in fitness-to-drive evaluation. Neurology 2023, in press.