Towards the automated detection of interictal epileptiform discharges with magnetoencephalography.
J Neurosci Methods
; 403: 110052, 2024 03.
Article
em En
| MEDLINE
| ID: mdl-38151188
ABSTRACT
BACKGROUND:
The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria. NEWMETHOD:
Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy.RESULTS:
In focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci-an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. COMPARISON WITH EXISTINGMETHODS:
We compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners.CONCLUSIONS:
IED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Epilepsias Parciais
/
Epilepsia
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article