Semi-Automated Detection, Annotation, and Prognostic Assessment of Ictal Chirps in Intracranial EEG from Patients with Epilepsy

We analyzed the spectro-temporal characteristics of ictal “chirp” events in intracranial EEG (iEEG) recordings from 13 epilepsy patients, using a custom derivative dataset of 22,721 spectrograms that we generated from the Epilepsy-iEEG-Multicenter Dataset. Ictal chirps, transient frequency-modulated patterns, were semi-automatically annotated to assess their relationship with seizure onset zones (SOZs) and surgical outcomes. Preprocessing included notch filtering (60 Hz, 120 Hz) and bandpass filtering (1–60 Hz), followed by segmentation into 60-second windows. Spectrograms were generated via Short-Time Fourier Transform (STFT) with a Hann window (87.5% overlap) and converted to dB scale. Chirps were annotated by manually drawing bounding boxes, followed by automated ridge detection, model fitting, and feature extraction (start/end time-frequency, duration, direction, RMSE, R ² ). Spatial, Spectro-temporal, and clinical features were analyzed using heatmaps, hierarchical clustering, statistical tests (Mann-Whitney U), and outcome prediction models. Patient-channel mappings revealed clustering of chirp patterns among specific patient pairs, correlating with shared clinical profiles. Flow-based analysis demonstrated prognostic value: very high spectral durations in SOZ regions were associated with favorable surgical outcomes (80.43% success rate), whereas very high temporal durations in SOZ correlated with poorer outcomes (51.35% risk). Statistical comparisons showed significant differences between SOZ and non-SOZ chirps: SOZ chirps exhibited longer spectral durations (10.13 ± 6.35 Hz vs. 8.51 ± 5.66 Hz, *p* < 0.001), shorter temporal durations (6.76 ± 5.83 s vs. 7.14 ± 5.39 s, *p* = 0.006), and higher spectro-temporal ratios (2.66 vs. 1.92, *p* < 0.001). Distribution analyses further indicated that prolonged temporal chirps were more prevalent in non-SOZ regions.