Stochastic Sparse Sampling: A Variable-Length Time Series Classification Framework for Seizure Onset Zone...

Variable-length time series classification (VTSC) problems are prevalent in healthcare applications, such as heart rate monitoring and electrophysiological recordings, where sequence length varies among patients and events. VTSC is challenging as finite-context models such as Transformers require padding, truncation, or interpolation, leading to distortion in the input data, higher computational costs, and overfitting, while infinite-context models including recurrent neural networks struggle with overcompression and unstable gradients over long sequences. In this paper, we develop a novel VTSC framework based on Stochastic Sparse Sampling (SSS) for seizure onset zone (SOZ) localization, a critical VTSC problem requiring identification of seizure-inducing brain regions from variable-length electrophysiological time series. The proposed framework sparsely samples time series windows to compute local predictions, which are then aggregated and calibrated to form a global prediction. SSS provides post-hoc insights into local signal characteristics related to the SOZ, by visualizing temporally averaged local predictions throughout the signal. We evaluate our method on the Epilepsy intracranial electroencephalography (iEEG) Multicenter Dataset, a heterogeneous collection of iEEG recordings obtained from four independent medical centers. The proposed solution outperforms state-of-the-art (SOTA) baselines across most medical centers, and superior performance on all out-of-distribution (OOD) unseen medical centers.