A novel ANN-based classification of spike-wave activity in 24-hour EEG recordings in rats using spectrograms: Spike-Wave Discharge Artificial Neural Network (SWAN).

Abstract

BACKGROUND: Electroencephalographic (EEG) detection of spike-wave discharges (SWDs) is essential for diagnosing absence epilepsy. Automated tools for long-term wearable EEG are needed, but current methods relying on basic signal variability metrics inadequately capture SWD complexity. NEW METHOD: We developed the Spike-Wave discharge Artificial Neural Network (SWAN), a shallow ANN classifier analyzing STFT spectrograms. SWAN addresses two critical dimensions of absence epilepsy: 1) spontaneous SWDs in WAG/Rij rats, and 2) drug-induced SWD transformations mediated by alpha2-adrenoreceptor agonists (xylazine, dexmedetomidine). RESULTS: Trained on baseline EEG from 3 rats and tested on baseline/pharmacological recordings from 4 rats, SWAN achieved high precision (0.96) and sensitivity (0.79) across both conditions. It incorporates a novel "certainty" metric quantifying detection confidence. COMPARISON WITH EXISTING METHODS: SWAN surpasses amplitude-based variability measures (e.g., standard deviation) by directly evaluating complex spatiotemporal SWD patterns in spectrograms, enabling more reliable detection. Its shallow architecture facilitates mathematical interrogation of SWD features. CONCLUSIONS: SWAN accurately identifies both spontaneous and pharmacologically transformed SWDs in a validated rat model. High precision minimizes over-diagnosis in prolonged recordings, while automation supports unattended monitoring via wearable devices. Future work requires expanded datasets to optimize sensitivity under pharmacological challenge. SWAN provides a robust tool for epilepsy research and therapeutic assessment.