Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification

• URL: http://arxiv.org/abs/2410.06412v2
• Date: Tue, 22 Oct 2024 03:11:31 GMT
• Title: Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification
• Authors: Xavier Mootoo, Alan A. Díaz-Montiel, Milad Lankarany, Hina Tabassum,
• Abstract summary: We propose $textbfS$tochastic $textbfS$parse $textbfS$ampling (SSS), a novel VTSC framework developed for medical time series. SSS manages variable-length sequences by sparsely sampling fixed windows to compute local predictions, which are then aggregated and calibrated to form a global prediction. We evaluate our method on the Epilepsy iEEG Multicenter dataset, a heterogeneous collection of intracranial electroencephalography (iEEG) recordings obtained from four independent medical centers.
• Score: 9.474649136535705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the majority of time series classification research has focused on modeling fixed-length sequences, variable-length time series classification (VTSC) remains critical in healthcare, where sequence length may vary among patients and events. To address this challenge, we propose $\textbf{S}$tochastic $\textbf{S}$parse $\textbf{S}$ampling (SSS), a novel VTSC framework developed for medical time series. SSS manages variable-length sequences by sparsely sampling fixed windows to compute local predictions, which are then aggregated and calibrated to form a global prediction. We apply SSS to the task of seizure onset zone (SOZ) localization, a critical VTSC problem requiring identification of seizure-inducing brain regions from variable-length electrophysiological time series. We evaluate our method on the Epilepsy iEEG Multicenter Dataset, a heterogeneous collection of intracranial electroencephalography (iEEG) recordings obtained from four independent medical centers. SSS demonstrates superior performance compared to state-of-the-art (SOTA) baselines across most medical centers, and superior performance on all out-of-distribution (OOD) unseen medical centers. Additionally, SSS naturally provides post-hoc insights into local signal characteristics related to the SOZ, by visualizing temporally averaged local predictions throughout the signal.

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