A Composable Channel-Adaptive Architecture for Seizure Classification

• URL: http://arxiv.org/abs/2512.19123v1
• Date: Mon, 22 Dec 2025 07:57:20 GMT
• Title: A Composable Channel-Adaptive Architecture for Seizure Classification
• Authors: Francesco Carzaniga, Michael Hersche, Kaspar Schindler, Abbas Rahimi,
• Abstract summary: We develop a channel-adaptive (CA) architecture that seamlessly processes time-series with an arbitrary number of channels.<n>We evaluate our CA-models on a seizure detection task both on a short-term (20 hours) and a long-term (2500 hours) dataset.
• Score: 7.0214066512269
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: We develop a channel-adaptive (CA) architecture that seamlessly processes multi-variate time-series with an arbitrary number of channels, and in particular intracranial electroencephalography (iEEG) recordings. Methods: Our CA architecture first processes the iEEG signal using state-of-the-art models applied to each single channel independently. The resulting features are then fused using a vector-symbolic algorithm which reconstructs the spatial relationship using a trainable scalar per channel. Finally, the fused features are accumulated in a long-term memory of up to 2 minutes to perform the classification. Each CA-model can then be pre-trained on a large corpus of iEEG recordings from multiple heterogeneous subjects. The pre-trained model is personalized to each subject via a quick fine-tuning routine, which uses equal or lower amounts of data compared to existing state-of-the-art models, but requiring only 1/5 of the time. Results: We evaluate our CA-models on a seizure detection task both on a short-term (~20 hours) and a long-term (~2500 hours) dataset. In particular, our CA-EEGWaveNet is trained on a single seizure of the tested subject, while the baseline EEGWaveNet is trained on all but one. Even in this challenging scenario, our CA-EEGWaveNet surpasses the baseline in median F1-score (0.78 vs 0.76). Similarly, CA-EEGNet based on EEGNet, also surpasses its baseline in median F1-score (0.79 vs 0.74). Conclusion and significance: Our CA-model addresses two issues: first, it is channel-adaptive and can therefore be trained across heterogeneous subjects without loss of performance; second, it increases the effective temporal context size to a clinically-relevant length. Therefore, our model is a drop-in replacement for existing models, bringing better characteristics and performance across the board.

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