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Anomaly Detection in Invasively Recorded Neuronal Signals Using Deep Neural Network: Effect of Sampling Frequency

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Applied Intelligence and Informatics (AII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1435))

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Abstract

Abnormality detection has advanced in recent years with the help of machine learning, in particular with deep learning models, which can predict accurately across many types of signals and applications. In the case of neuronal signals, abnormalities can present themselves as artefacts or manifestations of neurological diseases. Among the diverse neuronal pathologies, we chose to look at the detection of seizures, as they manifest as a brief anomaly in contrast to normal brain activity in the majority portion of the data during a prolonged recording. Epileptic patients benefit from portable systems, which are dependant on efficient energy consumption, and the sampling frequency of the signal is of vital importance element to its battery lifespan. In this article, the impact of the sampling rate on a deep learning-based multi-class classification model is explored via the use of an open-source seizure dataset.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Nottingham Trent University, Clifton Lane, Clifton, Nottingham, NG11 8NS, UK

    Marcos Fabietti, Mufti Mahmud & Ahmad Lotfi

  2. Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Clifton, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  3. Computing and Informatics Research Center, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

Authors
  1. Marcos Fabietti
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  2. Mufti Mahmud
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  3. Ahmad Lotfi
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Corresponding author

Correspondence to Mufti Mahmud .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Fabietti, M., Mahmud, M., Lotfi, A. (2021). Anomaly Detection in Invasively Recorded Neuronal Signals Using Deep Neural Network: Effect of Sampling Frequency. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-82269-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82268-2

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