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Epileptic seizure detection using convolutional neural networks and recurrence plots of EEG signals

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Abstract

Epilepsy is a neurological disorder causing abnormal activities in the brain such as seizures, unusual behavior, sensations and loss of awareness. This disorder can be diagnosed with help of the Electroencephalogram (EEG) which evaluates the electrical activity in the brain, which is considered a dynamical system. The epileptic neuronal networks are made up of complex non-linear structures whose non-linear behavior manifests in the EEG signal. Due to the chaotic and non-linear nature of the EEG signal, we propose the use of Recurrence Plots (RP) to capture the non-linear dynamics in the EEG. Recurrence is a fundamental property of dynamical systems and contains information about the system behavior. The Recurrence Plots are a tool for the visualization and analysis of the dynamic system’s behavior. The ResNet ensemble trained on these Recurrence Plots attains cent percent accuracy in most class combination scenarios such as normal vs epileptic or pre-ictal vs ictal. Likewise, the sensitivity and specificity are also 100% in most class combination scenarios. Such a model can assist in the diagnosis of the disease and can also give an early alert to the patient on the onset of seizure.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

    Sriya Ravi, Shreenidhi S,  N. Ilakiyaselvan & A. Nayeemulla Khan

  2. Dept. of IT, Sri Sivasubramanya Nadar College of Engineering, Kalavakkam, Tamil Nadu, 603110, India

    A. Shahina

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  1. Sriya Ravi
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  2. Shreenidhi S
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  3. A. Shahina
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  4. N. Ilakiyaselvan
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  5. A. Nayeemulla Khan
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Correspondence to N. Ilakiyaselvan.

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Ravi, S., S, S., Shahina, A. et al. Epileptic seizure detection using convolutional neural networks and recurrence plots of EEG signals. Multimed Tools Appl 81, 6585–6598 (2022). https://doi.org/10.1007/s11042-021-11608-2

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  • DOI: https://doi.org/10.1007/s11042-021-11608-2

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