Interictal high-frequency oscillations, including ripples in the frequency range of 80-250 hertz and fast ripples between 250-500 hertz, are increasingly recognized as reliable electrophysiological biomarkers for delineating the epileptogenic zone in patients with drug-resistant epilepsy. However, their routine clinical exploitation remains limited due to pronounced morphological variability, low signal-to-noise ratios, and the difficulty of identifying overlapping events in which ripples and fast ripples occur simultaneously.
This paper presents an automated deep learning framework designed for the multi-label classification of pathological high-frequency oscillations in intracranial electroencephalographic signals. The proposed approach integrates advanced nonlinear statistical descriptors, including entropy- and complexity-based measures, in order to enhance the discriminative representation of the signals. These features are processed using a hybrid deep learning architecture that combines convolutional neural networks for local morphological feature extraction with bidirectional long short-term memory networks to capture long-range temporal dependencies in non-stationary neural signals.
The proposed framework was evaluated using the publicly available multi-patient intracranial electroencephalography dataset provided by the Collaborative Research in Computational Neuroscience initiative. Experimental results demonstrate a classification accuracy of 98.3 %, along with high precision and balanced performance across all pathological classes. These findings indicate that the proposed method offers a robust and objective solution for the automated identification of high-frequency oscillations, with strong potential for improving presurgical evaluation and decision-making in epilepsy surgery.