A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

  • Farnaz ForooghifarEmail author
  • Amir Aminifar
  • Leila Cammoun
  • Ilona Wisniewski
  • Carolina Ciumas
  • Philippe Ryvlin
  • David Atienza


Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to time and location. In this paper, we propose a self-aware wearable system for real-time detection of epileptic seizures on a long-term basis. First, we propose a multi-parametric machine learning technique to detect seizures by analyzing both cardiac and respiratory responses to seizures, which are obtained using only the ECG signal. Second, in order to enable long-time epilepsy detection, we introduce the notion of self-awareness in our real-time wearable system. We evaluate the performance of our proposed solution based on an epilepsy database of more than 211 hours of recording, provided by the Lausanne University Hospital (CHUV), on the INYU wearable sensor. Our proposed system achieves a sensitivity of 88.66% and a specificity of 85.65% before applying self-awareness. Moreover, by controlling the energy-quality trade-offs using our self-aware energy-management technique, we can tune the battery lifetime of the wearable system to last between 67.55 and 136.91 days while, still outperforming the state-of-the-art techniques for wearable seizure detection, by achieving from 85.54% to 79.33% geometric mean of specificity and sensitivity.


Epileptic seizure detection Cardiac system Respiratory system Self-awareness Electrocardiography Heart-rate variability 



This work has been partially supported by the MyPreHealth research project (Hasler Foundation project No. 16073), the WiTNESS project (Promobilia Foundation project No. 18079), and the Human Brain Project (HBP) SGA2 (GA No. 785907). The authors also would like to thank M. Nassralla for an initial implementation of a subset of the seizure detection features for epileptic signals, which was revised and extended to define the final set of used features.


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Authors and Affiliations

  1. 1.Embedded Systems Laboratory (ESL)Swiss Federal Institute of Technology Lausanne (EPFL)LausanneSwitzerland
  2. 2.Department of Clinical NeuroscienceLausanne University Hospital (CHUV)LausanneSwitzerland

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