Evaluation of channel performance in seizure prediction

Hoai An Nguyen, Zhinoos Razavi Hesabi, Levin Kuhlmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Since unprovoked seizures are identified as the biggest concern of epileptic patients, an effective seizure prediction device is definitely a game changer in epilepsy management. In order to apply seizure prediction in practical settings, seizure prediction algorithms are mandated to be optimized prior to installation into implantable devices which often come with low computational resources. To achieve this goal, past literature has demonstrated various machine learning approaches to reduce the number of channels or features needed in seizure prediction. However, the resultant channel or feature set generated by machine learning models can change over time, thus making the device configuration unfeasible once the device is implanted. Therefore, we evaluated the seizure prediction performance of each channel in a set of intracranial electrode-based electroencephalography (iEEG) recordings to uncover if electrode placement impacts on the predictive accuracy. The study was conducted on a dataset of three patients from the first-in-man seizure warning device trial and a state-of-the-art seizure prediction algorithm based on Extra Trees classification was applied. In three patients, the best channels achieved the area under the receiver-operating curve (AUC) scores of 0.77, 0.70, and 0.76, respectively. The result also indicated superior seizure prediction of some channels across all patients. Regarding AUC comparison between the best performing channel and all channels, in patient 1, using one channel as a single predictor surpassed the multiple channel model. While statistically significant differences were not obtained in patient 2, the one channel model offered practical benefits by preventing data overfitting and reducing computational complexity. In patient 3, the general model performed better than individual channels. These findings demonstrate the feasibility of selecting electrodes to build models in some patients and also emphasize the importance of patient-specific methods for seizure prediction.

Original languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference 2021 (ACSW 2021)
EditorsNigel Stanger, Veronica Liesaputra Joachim
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781450389563
DOIs
Publication statusPublished - 2021
EventAustralasian Workshop on Health Informatics and Knowledge Management (HIKM) 2021 - Virtual, Dunedin, New Zealand
Duration: 1 Feb 20214 Feb 2021
http://www.hikm.net.au (Website)
https://dl.acm.org/doi/proceedings/10.1145/3437378 (Proceedings)

Conference

ConferenceAustralasian Workshop on Health Informatics and Knowledge Management (HIKM) 2021
Abbreviated titleHIKM 2021
CountryNew Zealand
CityDunedin
Period1/02/214/02/21
Internet address

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