ABSTRACT
Objective
This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs)
Methods
We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method.
Results
The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification.
Conclusions
HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs.
Significance
Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
HIGHLIGHTS
HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detector
HFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathological
A deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods
Full Text Availability
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