BioLT (streaming long-context biosignal modeling)

Clinical biosignals such as ECG, PPG, and EEG are increasingly acquired as hours-long streams from bedside monitors and wearables, yet existing analysis pipelines almost universally operate on isolated short windows of around 10 seconds and cannot process the long, irregular, frequently interrupted signals. We introduce BioLT, a streaming decoder-only framework for long-context biosignal modeling that turns any window-level feature extractor or pretrained biosignal foundation model into a streaming model. BioLT combines (i) a discontinuity-aware encoding that exposes the inter-window elapsed time as an explicit input channel, so that the decoder can learn to model these gaps as clinically meaningful information, and (ii) a fixed-size compressed memory bank supervised with cumulative summary statistics, which keeps both training and inference linear in the recording length while preserving long-range context. We evaluate on CPSC-2021, VitalDB, MC-MED, and CHB-MIT for arrhythmia, sepsis, and seizure detection and for forecasting up to 20 minutes ahead, using domain features and embeddings from six biosignal foundation models. Across 21 dataset-feature settings, BioLT achieves the highest detection AUROC in 15 and the highest 20-minute forecasting AUROC in 13 settings, and shows consistent performance gains over state-of-the-art snapshot foundation-model embeddings. These highlight the importance of modeling how streaming biosignal representations evolve over time, beyond improving window-level representations alone.

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