Channel-Level Seizure Onset and Offset Detection in Stereo-EEG with Sub-Second Temporal Precision Using Machine Learning

Abstract

The analysis of stereo-EEG (SEEG) recordings is integral to the surgical treatment of patients with drug-resistant epilepsy. These data, recorded from intracerebral depth electrodes, provide the spatiotemporal resolution necessary to identify the seizure onset zone and characterize the epileptogenic network. While prior machine learning approaches have demonstrated strong performance on seizure detection benchmarks, they lack the resolution and computational scalability required to precisely localize seizure onset and offset for each individual recording channel. We propose a two-stage framework for automated channel-level seizure detection in SEEG recordings with sub-second precision. Our model combines a permutation-based coarse-grain detector that flags potential seizure events within the recording and a fine-grain detector that performs window-level classification within each detected interval to identify seizure onset and offset for each channel. Our dataset contained of 5723 hours of SEEG recordings and 677 annotated seizure events from 26 patients who underwent SEEG at Massachusetts General Hospital. When evaluated on a held-out test cohort of 12 seizure events from three patients, the coarse-grain detector achieved an F1 score of 0.608 and the fine-grain detector achieved an F1 score of 0.16 on the window level classification task. The fine-grain detector's low F1 score was driven by low precision, reflecting a tendency to over-predict seizure windows within intervals flagged by the coarse-detector. We also train and evaluate the fine-grain detector on the Bonn EEG dataset, where it achieves an F1 score of 0.981. Although the Bonn dataset is not directly comparable to our task of sub-second channel-level seizure onset and offset detection, it demonstrates strong performance on standard seizure classification tasks. In summary, this work combines a computationally efficient coarse-grain detector with a transformer-based fine-grain classifier for automated channel-level seizure detection in clinical SEEG recordings. This framework enables the generation channel-level seizure onset and offset predictions at the spatial and temporal resolution required for clinical analyses of the epileptic network while also addressing the computational constraints of analyzing large-scale multi-channel SEEG data.