Detection of High-Frequency Oscillations from Intracranial EEG Data with Switching State Space Model.
bioRxiv
; 2024 May 04.
Article
en En
| MEDLINE
| ID: mdl-38746474
ABSTRACT
High Frequency Oscillations (HFOs) is an important biomarker that can potentially pinpoint the epileptogenic zones (EZs). However, the duration of HFO is short with around 4 cycles, which might be hard to recognize when embedded within signals of lower frequency oscillatory background. In addition, annotating HFOs manually can be time-consuming given long-time recordings and up to hundreds of intracranial electrodes. We propose to leverage a Switching State Space Model (SSSM) to identify the HFOs events automatically and instantaneously without relying on extracting features from sliding windows. The effectiveness of the SSSM for HFOs detection is fully validated in the intracranial EEG recording from human subjects undergoing the presurgical evaluations and showed improved accuracy when capturing the HFOs occurrence and their duration.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
BioRxiv
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos