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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: Online First},
        keywords={Multi-modal, Seizure Detection, Person-specific, Classification, Epilepsy},
  • J. Pordoy
    Y. Zhang
    N. Matoorian
    M. Zolgharni
    Year: 2021
    Seizure Classification Using Person-Specific Triggers
    DOI: 10.4108/eai.4-2-2021.168650
J. Pordoy1,*, Y. Zhang1, N. Matoorian1, M. Zolgharni1
  • 1: School of Computing and Engineering, University of West London
*Contact email:


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.