Ustanowienie indywidualnego dla pacjenta punktu odniesienia EEG metodą e-norms do wykrywania napadów u dzieci bez etykietowanych danych treningowych
Patient-Specific EEG Baseline Establishment Using the E-norms Method for Pediatric Seizure Detection Without Labeled Training Data
W skrócie
[Preprint - wstępne wyniki] Badacze opracowali nową metodę wykrywania napadów epilepsji u dzieci na podstawie zapisu EEG, która nie wymaga wcześniejszego trenowania na przykładach napadów. Metoda osiągnęła 94,4% skuteczność w wykrywaniu napadów i działa bez uniwersalnego progu - każdy pacjent ma swój indywidualny próg dostosowany do jego mózgu. To podejście przewyższa umiejętności lekarzy rozpoznających napady oraz inne automatyczne systemy, co daje nadzieję na lepsze monitorowanie epilepsji u najmłodszych pacjentów.
Oryginalny abstract (angielski)
The aim of this work is to validate patient-specific EEG baseline establishment using the e-norms method as a screening and retrospective-review tool for seizure detection in pediatric epilepsy. The method was applied to 247 seizure-free EEG recordings (263.92 hours) from 10 patients in the CHB-MIT Scalp EEG Database (ages 3-18). A composite stability metric combining first-derivative dynamics, spectral entropy, variance, and line length was computed per 2-second epoch across 23 channels. Patient-specific detection thresholds were derived from each patient's seizure-free baseline using a weighted statistical procedure. Performance was validated against 72 expert-annotated seizures (2,705 epochs) across 62 seizure files, with durations spanning 6 to 264 seconds (44-fold range). The results show that detection achieved 94.4% event-level sensitivity (68 of 72 seizures; 95% CI 86.6-97.8%) and 81.5% epoch-level sensitivity (2,204 of 2,705 epochs; 95% CI 80.0-82.9%). Eight of ten patients achieved 100% event-level sensitivity with epoch-level sensitivity ranging from 58.7% to 100.0%. Two patients showed partial event-level failures (CHB-15: 17 of 20; CHB-18: 5 of 6), with the four missed events attributable to two characterizable failure modes. Patient-specific thresholds ranged from 4.06 to 4.81 (mean 4.51 +/- 0.25); threshold variation did not correlate reliably with age or sex, confirming that no universal threshold could achieve comparable performance. Detection margins ranged from 0.88 to 1.24 times. Patient-specific e-norms achieves 94.4% event-level sensitivity for pediatric EEG seizure detection without requiring labeled seizure training data, exceeding published human expert inter-rater agreement (50-76%) and recent automated approaches in adult cohorts using behind-the-ear EEG and wearable ECG. Two characterizable failure modes account for the four missed events and inform appropriate clinical use. As a high-sensitivity screening tool complementary to real-time alarm systems, the method is ready for adult validation, prospective deployment, and head-to-head benchmarking.