Sleep2Word: Translating Peripheral to EEG using Physiological Wordization

Estimating full-night electroencephalography (EEG) spectrograms from peripheral measurements could extend EEG-informed sleep analysis to recordings acquired without EEG. This task requires compact representations of signals with different temporal structures. We introduce Sleep2Word, a framework for translating respiration, electrocardiography (ECG), or photoplethysmography (PPG) into EEG spectrograms through duration-aware physiological words. Building on context-dependent chunking, Sleep2Word groups physiological embeddings into variable-duration chunks and jointly quantizes their content and duration into modality-specific wordbooks. A block-autoregressive translator then predicts target EEG word identities and occurrence-specific duration refinements within fixed-duration blocks. This design accommodates independent source and target wordbooks, limits temporal drift, and shortens the autoregressive decoding path. Trained on over 20K PSG cases across 9 datasets, Sleep2Word consistently outperforms existing spectrogram generation baselines in EEG spectrogram reconstruction, sleep staging, arousal detection, and survival prediction. Compressing full night sequences by up to 69.7%, the learned words yield a favorable accuracy–compute trade-off. Sleep event analysis further shows that the learned words capture physiologically plausible associations across modalities, suggesting their potential for interpretable cross-modal analysis.