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Trends and Challenges of Processing Measurements from Wearable Devices Intended for Epileptic Seizure Prediction

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Abstract

The rapid contemporary development of wearable devices offers non-invasive and effective approaches for monitoring the human brain. Recent studies have investigated the prediction of epileptic seizures (ESs) using wearable measurements, such as scalp electroencephalography and functional near-infrared spectroscopy. The signal processing tasks are the core component of emerging closed-loop ES prediction (ESP) systems. Various research groups have introduced many state-of-the-art signal processing techniques to improve ESP performance. Wearable measurements consider low frequency and low spatial resolution characteristics. In this paper, we provide a comprehensive review of signal processing techniques including preprocessing, feature extraction, dimensionality reduction and classification schemes for ESP systems. Trends and concerns of ESP studies at the end of the manuscript.

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Acknowledgments

Authors would like to acknowledge funding support from Westlake University, Zhejiang Key R&D Program No. 2021C03002 and Bright Dream Joint Institute for Intelligent Robotics.

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Authors and Affiliations

  1. Zhejiang University, Zhejiang, 310058, Hangzhou, China

    Yankun Xu

  2. CenBRAIN Lab., School of Engineering, Westlake University, Zhejiang, 310024, Hangzhou, China

    Yankun Xu, Jie Yang & Mohamad Sawan

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  1. Yankun Xu
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Correspondence to Mohamad Sawan.

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Xu, Y., Yang, J. & Sawan, M. Trends and Challenges of Processing Measurements from Wearable Devices Intended for Epileptic Seizure Prediction. J Sign Process Syst 94, 527–542 (2022). https://doi.org/10.1007/s11265-021-01659-x

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  • DOI: https://doi.org/10.1007/s11265-021-01659-x

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