Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy

iScience, vol. 24, pp. 102538

Abstract

A range of abnormal electrical activity patterns termed epileptiform discharges can occur in the brains of persons with epilepsy. These epileptiform discharges can be monitored and recorded with implanted devices that deliver therapeutic neurostimulation. These continuous recordings provide an opportunity to study the behavioral correlates of epileptiform discharges as the patients go about their daily lives. Here, we captured the smartphone touchscreen interactions in eight patients in conjunction with electrographic recordings (accumulating 35,714 h) and by using an artificial neural network model addressed if the behavior reflected the epileptiform discharges. The personalized model outputs based on smartphone behavioral inputs corresponded well with the observed electrographic data (R: 0.2-0.6, median 0.4). The realistic reconstructions of epileptiform activity based on smartphone use demonstrate how day-to-day digital behavior may be converted to personalized markers of disease activity in epilepsy.

Authors 4

1. Robert B. Duckrow

   Yale University

   Affiliation as printed

   Department of Neurology, Yale University, New Haven, CT, USA

2. Enea Ceolini Aachen Institute of Psychology, Leiden University

   Leiden University

   Affiliation as printed

   Institute of Psychology, Leiden University, Wassenaarseweg 52, Leiden 2333 AK, The Netherlands

   QuantActions GmbH, Lausanne, Switzerland

3. Hitten P. Zaveri

   Yale University

   Affiliation as printed

   Department of Neurology, Yale University, New Haven, CT, USA

4. Arko Ghosh corresponding Aachen Institute of Psychology, Leiden University

   Leiden University

   Affiliation as printed

   Institute of Psychology, Leiden University, Wassenaarseweg 52, Leiden 2333 AK, The Netherlands