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An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

Author

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  • Mohammadali Sharifshazileh

    (Institute of Neuroinformatics, University of Zurich and ETH Zurich
    University Hospital Zurich, University of Zurich)

  • Karla Burelo

    (Institute of Neuroinformatics, University of Zurich and ETH Zurich
    University Hospital Zurich, University of Zurich)

  • Johannes Sarnthein

    (University Hospital Zurich, University of Zurich)

  • Giacomo Indiveri

    (Institute of Neuroinformatics, University of Zurich and ETH Zurich)

Abstract

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

Suggested Citation

  • Mohammadali Sharifshazileh & Karla Burelo & Johannes Sarnthein & Giacomo Indiveri, 2021. "An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23342-2
    DOI: 10.1038/s41467-021-23342-2
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    Cited by:

    1. Rui Yuan & Pek Jun Tiw & Lei Cai & Zhiyu Yang & Chang Liu & Teng Zhang & Chen Ge & Ru Huang & Yuchao Yang, 2023. "A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Romain Beaubois & Jérémy Cheslet & Tomoya Duenki & Giuseppe De Venuto & Marta Carè & Farad Khoyratee & Michela Chiappalone & Pascal Branchereau & Yoshiho Ikeuchi & Timothée Levi, 2024. "BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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