arXiv Machine Learning

Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals

arXiv:2510. 09764v2 Announce Type: replace Abstract: Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes.

arXiv AI

Jun 19

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

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.

By Feng Wu, Harsh Deep, Eric Lehman, Sanyam Kapoor, Guoshuai Zhao, Rahul Krishnan, Gari Clifford, Li-wei H Lehman

benchmarks

arXiv Machine Learning

Jul 2

A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data

arXiv:2607. 01145v1 Announce Type: new Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv Machine Learning

Jul 16

Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis

arXiv:2607. 01145v2 Announce Type: replace Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv Machine Learning

Jun 9

Self-Supervised Dynamical System Representations for Physiological Time-Series

arXiv:2512. 00239v2 Announce Type: replace Abstract: The effectiveness of self-supervised learning (SSL) for physiological time series depends on the ability of a pretraining objective to preserve information about the underlying physiological state while filtering out unrelated noise.

By Yenho Chen, Maxwell A. Xu, James M. Rehg, Christopher J. Rozell

fine-tuning

arXiv Machine Learning

5d ago

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

arXiv:2608. 12944v1 Announce Type: new Abstract: Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the shared physiology across sensors unexploited.

By Hamza Shafiq, Hung Manh Pham, Bin Zhu, Pan Zhou, Jun Hu, Aaqib Saeed

llms rag multimodal benchmarks

arXiv Machine Learning

5d ago

The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning

arXiv:2608. 12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences.

By Ahmed Sameh, Ramzi Al-Sharawi, Yogatheesan Varatharajah

llms rag efficiency

arXiv AI

Jun 8

A robust PPG foundation model using multimodal physiological supervision

arXiv:2606. 07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings.

By Eloy Geenjaar, Vince Calhoun, Scott Daly, Gouthaman KV, Lie Lu, Trisha Mittal, Daniel P. Darcy

multimodal benchmarks

arXiv AI

1d ago

Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning

arXiv:2608. 14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability.

By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski

multimodal

arXiv Machine Learning

Aug 5

DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing

arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.

By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen

rag multimodal benchmarks

arXiv AI

Jun 9

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.

By Jonathan F. Carter, Lionel Tarassenko

llms rag efficiency multimodal benchmarks

arXiv AI

Jul 17

VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence

arXiv:2607. 14510v1 Announce Type: new Abstract: Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines.

By Haiteng Wang, Jingheng Yan, Xiaokang Wang, Lei Ren

llms multimodal benchmarks safety

arXiv AI

Jun 16

CAP: Towards PPG Universal Representation Learning with Patient-level Supervision

arXiv:2606. 15284v1 Announce Type: cross Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support.

By Chenyang He, Xinyi Shao, Shun Huang, Bosong Huang, Daoqiang Zhang, Ming Jing, Cheng Ding

multimodal benchmarks safety

Related stories