SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models

📰 ArXiv cs.AI

Learn to apply self-supervised learning to physiological waveforms using structured state space models, improving analysis of medical time series data

advanced Published 19 Jun 2026

Action Steps

Apply self-supervised learning techniques to physiological waveform data
Use structured state space models to capture long-sequence dependencies
Configure encoder architectures, such as convolutional neural networks, for optimal performance
Test the approach on unlabeled medical time series data
Compare results with traditional supervised learning methods

Who Needs to Know This

Data scientists and researchers working with medical time series data, such as ECGs, can benefit from this approach to improve their analysis and modeling capabilities

Key Insight

💡 Self-supervised learning with structured state space models can effectively capture long-sequence dependencies in physiological waveforms

Key Takeaways

Learn to apply self-supervised learning to physiological waveforms using structured state space models, improving analysis of medical time series data

Full Article

Title: SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models

Abstract:
arXiv:2606.19888v1 Announce Type: cross Abstract: Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data. While recent self-supervised learning (SSL) methods, based on various encoder architectures such as convolutional neural networks, have been proposed to learn representations from unlabeled data, they often fall short in capturing long-

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