Hypnos: Revolutionizing Physiological Signal Processing with Next-Token Prediction

Foundation models have shown immense potential in compressing multi-modal physiological signals into compact representations of human health. Their applications span various medical fields including sleep medicine, cardiology, and neurology. Traditional approaches have relied heavily on masked-reconstruction or contrastive objectives. Yet, these methods have limitations. Masked reconstruction struggles with the unpredictable nature of physiological signals, and contrastive approaches falter due to the poorly understood semantic invariances of these signals.

Introducing Hypnos

Enter Hypnos, a novel approach that tackles these challenges head-on through next-token prediction. This technique is both simple and scalable, offering a fresh perspective on representation learning. Hypnos is trained using data from over 20,000 overnight polysomnography recordings across eight different sensing modalities, including EEG, ECG, and respiratory signals. The model tokenizes each modality into streams of discrete tokens using residual vector quantization, then employs a large auto-regressive RQ-Transformer to predict the next token across all modalities in parallel.

Why Hypnos Matters

Hypnos isn't just another model, it's a major shift in how we process physiological signals. It significantly outperforms current foundation models, proving its mettle in sleep stage classification, achieving comparable results with considerably less labeled data. Specifically, Hypnos uses 100 times less labeled data than traditional supervised methods while still matching their performance on held-out test sets. The real kicker? Hypnos also generalizes beyond sleep-related applications, showing prowess in daytime physiological tasks like detecting atrial fibrillation, where it surpasses a dedicated ECG foundation model.

The Future of Physiological Signal Processing

Hypnos' success raises a critical question: will next-token prediction become the norm in physiological signal processing? Its ability to provide strong embeddings for downstream tasks using minimal labeled data is compelling. This approach could redefine how healthcare professionals interpret complex physiological signals, potentially leading to more accurate diagnoses and personalized treatment plans.

The paper's key contribution is demonstrating next-token prediction's viability as a self-supervised objective. This method could be the catalyst for a new wave of innovation in medical AI. With code and data available for scrutiny, Hypnos sets a new standard for reproducibility and opens the door for further exploration in multi-modal signal processing.