Pordoy, Jamie, Zhang, Ying ORCID: https://orcid.org/0000-0002-6669-1671 and Matoorianpour, Nasser (2021) A Novel Approach for Seizure Classification Using Patient Specific Triggers: Pilot Study. In: Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020), 16-18 Oct 2020, Shanghai, China.

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Abstract

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 a need for a personalised method of detecting epileptic seizures. Epidemiological studies show that up to 91% of those diagnosed identify one or more triggers as the causation of their seizure onset. These triggers are patient-specific and can affect those diagnosed in different ways dependent on each person’s 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. Therefore, this pilot study investigates the use of patient-specific triggers (PST) in diagnosed epileptics, and whether they can be used as an additional modality when detecting seizures. This study used a precision medicine approach with artificial intelligence (AI), to train and test several patient-specific algorithms that classified epileptic seizures based on the PST of each participant. Experimental 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.

Item Type:	Conference or Workshop Item (Paper)
ISBN:	9783030675400
Identifier:	10.1007/978-3-030-67540-0_29
Page Range:	pp. 455-468
Identifier:	10.1007/978-3-030-67540-0_29
Subjects:	Computing
Related URLs:	Publisher
Depositing User:	Marc Forster
Date Deposited:	12 Nov 2024 14:13
Last Modified:	12 Nov 2024 14:13
URI:	https://repository.uwl.ac.uk/id/eprint/12891

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