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

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Abstract

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.

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Acknowledgements

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. Embedded Systems Laboratory (ESL), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Farnaz Forooghifar, Amir Aminifar & David Atienza

  2. Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), Lausanne, Switzerland

    Leila Cammoun, Ilona Wisniewski, Carolina Ciumas & Philippe Ryvlin

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  1. Farnaz Forooghifar
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  2. Amir Aminifar
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  3. Leila Cammoun
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  5. Carolina Ciumas
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  7. David Atienza
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Correspondence to Farnaz Forooghifar.

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Forooghifar, F., Aminifar, A., Cammoun, L. et al. A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection. Mobile Netw Appl 27, 677–690 (2022). https://doi.org/10.1007/s11036-019-01322-7

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