arXiv:2608.21147v1 Announce Type: new Abstract: The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a pa...
The paper introduces a self‑supervised method for detecting end‑diastole (ED) and end‑systole (ES) in echocardiography by constraining the latent motion to a single‑parameter orbit, effectively modeling cardiac phase as a one‑dimensional signal. This approach yields an interpretable representation that directly identifies ED and ES, improving ED localisation and matching ES performance compared to prior state‑of‑the‑art methods, while using fewer training epochs and a more constrained model. The method is trained on EchoNet‑Dynamic without annotations and the code is publicly available.
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient p...
The paper introduces Diffusion-Conditioned Representation Alignment (DCRA), a training framework that uses the forward diffusion process as a structured corruption scheduler for time‑series representation learning. DCRA aligns representations across noise levels with a feature‑level consistency objective, preserving class‑discriminative structure and enabling smooth, semantically coherent trajectories in latent space. Experiments on the CHB‑MIT EEG dataset demonstrate that DCRA improves seizure detection performance under various noise conditions, achieving higher sensitivity at low false‑positive rates and producing more balanced, structured representations than baseline methods.
arXiv:2607. 00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost.
arXiv:2606. 18496v1 Announce Type: cross Abstract: Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either.
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.
arXiv:2608. 12592v1 Announce Type: new Abstract: Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient.
The paper introduces PI-AMFM, a permutation‑invariant neural framework that learns to decompose biomedical signals into variable‑cardinality amplitude‑ and frequency‑modulated (AM‑FM) modes. It combines a multiscale temporal encoder, a Mamba backbone, and component‑presence estimation, using Hungarian matching during training to handle unknown numbers of components. Experiments on synthetic AM‑FM signals and photoplethysmographic recordings show that PI‑AMFM outperforms existing methods in decomposition accuracy and successfully captures cardiac and respiratory dynamics without prior knowledge of mode count.
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.
RDDMPI introduces a residual denoising diffusion model for multivariate time series imputation. By decomposing the missing signal into a baseline reconstruction and a residual uncertainty component, the method conditions the diffusion process on both the completed signal and its latent representation, using a reliability-aware mechanism to balance baseline influence. Experiments on benchmark datasets show that this approach improves reconstruction accuracy and uncertainty quantification compared to prior diffusion-based methods.
arXiv:2608.24027v1 Announce Type: new Abstract: 4D medical image interpolation aims to recover missing volumes from sparsely observed time points and is important for dynamic anatomical analysis in a...