A Composable Channel-Adaptive Architecture for Seizure Classification

Multi-variate time-series are one of the primary data modalities involved in large classes of problems, where deep learning models represent the state-of-the-art solution. In the healthcare domain electrophysiological data, such as intracranial electroencephalography (iEEG), is used to perform a variety of tasks. However, iEEG models require that the number of channels be fixed, while iEEG setups in clinics are highly personalized and thus vary considerably from one subject to the next. To address this concern, we propose a channel-adaptive (CA) architecture that seamlessly functions on any multi-variate signal with an arbitrary number of channels. Each CA-model can be pre-trained on a large corpus of iEEG recordings from multiple heterogeneous subjects, and then finetuned to each subject using equal or lower amounts of data compared to existing state-of-the-art models, and in only 1/5 of the time. We evaluate our CA-models on a seizure detection task both on a short-term ($\sim$15 hours) and a long-term ($\sim$2600 hours) dataset. In particular, our CA-EEGWaveNet - based on EEGWaveNet - is trained on a single seizure of the tested subject, while the baseline EEGWaveNet is trained on all but one. CA-EEGWaveNet surpasses the baseline in median F1-score (0.78 vs 0.76). Similarly, CA-EEGNet - based on EEGNet - also surpasses its baseline (0.79 vs 0.74). Overall, we show that the CA architecture is a drop-in replacement for existing seizure classification models, bringing better characteristics and performance across the board.