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cc 22(14): e3

Research Article

Seizure Classification Using Person-Specific Triggers

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  • @ARTICLE{10.4108/eai.4-2-2021.168650,
        author={J. Pordoy and Y. Zhang and N. Matoorian and M. Zolgharni},
        title={Seizure Classification Using Person-Specific Triggers},
        journal={EAI Endorsed Transactions on Collaborative Computing},
        volume={4},
        number={14},
        publisher={EAI},
        journal_a={CC},
        year={2021},
        month={2},
        keywords={Multi-modal, Seizure Detection, Person-specific, Classification, Epilepsy},
        doi={10.4108/eai.4-2-2021.168650}
    }
    
  • J. Pordoy
    Y. Zhang
    N. Matoorian
    M. Zolgharni
    Year: 2021
    Seizure Classification Using Person-Specific Triggers
    CC
    EAI
    DOI: 10.4108/eai.4-2-2021.168650
J. Pordoy1,*, Y. Zhang1, N. Matoorian1, M. Zolgharni1
  • 1: University of West London
*Contact email: pord.jam@uwl.ac.uk

Abstract

Introduction: With advancements in personalised medicine, healthcare delivery systems have moved away from the one-size-fits-all approach, towards tailored treatments that meet the needs of individuals and specific subgroups. As nearly one-third of those diagnosed with epilepsy are classed as refractory and are resistant to antiepileptic medication, there is need for a personalised method of detecting epileptic seizures. Epidemiological studies show that up to 91% of those diagnosed identify one or more epilepsy related trigger as the causation behind their seizure onset. These triggers are person-specific and affect those diagnosed in different ways dependent on their idiosyncratic tolerance and threshold levels. Whilst these triggers are known to induce seizure onset, only a few studies have even considered their use as a preventive component, and whether they could be used as an additional sensing modality for non-EEG detection mechanisms. Objectives: 1. To record person-specific triggers (PST) from participants using IoT-enabled sensors and smart devices. 2. To train and test several dedicated machine learning models using a single participants data, 3. To conduct a comparative analysis and evaluate the performance of each model, 4. Formulate a conclusion as to whether PST could be used to improve on current methods of non-EEG seizure detection. Methodology: This study uses a precision approach combined with machine learning, to train and test several dedicated algorithms that can predict epileptic seizures. Each model is designed for a single participant, enabling a personalised method of classification unseen in non-EEG detection research. Results: Our results show accuracy, sensitivity, and specificity scores of 94.73%, 96.90% and 93.33% for participant 1 and 96.87%, 96.96% and 96.77% for participant 2, respectively. Conclusion: To conclude, this preliminary study has observed a noticeable correlation between the documented triggers and each participants seizure onset, indicating that PST have the potential to be used as an additional non-EEG sensing modality when classifying epileptic seizures.

Keywords
Multi-modal, Seizure Detection, Person-specific, Classification, Epilepsy
Received
2020-11-20
Accepted
2020-12-18
Published
2021-02-04
Publisher
EAI
http://dx.doi.org/10.4108/eai.4-2-2021.168650

Copyright © 2021 J. Pordoy et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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