TinyCardioUNet is a lightweight UNet designed to translate inertial measurement unit (IMU) data into electrocardiography (ECG) signals. It processes all six IMU axes, refines its bottleneck with a graph neural network that captures inter‑axis dependencies, and reduces parameters via tensor decomposition with variational Bayesian rank selection. On a public dataset it achieves an RMSE of 0.098 and a Pearson correlation of 0.677 with only 36.0 k parameters, while maintaining robustness to additive noise.
By Seungwoo Han, Ingon Chanpornpakdi, Motoi Noda, Puwadej Leelasiri, Ibuki Hiruma, Toshihisa Tanaka
arXiv:2609.05698v1 Announce Type: cross
Abstract: The paper introduces a neural network-based approach for analyzing ECG signals to estimate respiratory rate by leveraging the phe- nomenon of Respira...
By Julian Szymanski, Patryk Orkisz, Higinio Mora
arXiv:2605. 31249v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clinical report generation.
By Bosong Huang, Panzhen Zhao, Zengxiang Li, Patricia Lee, Wei Jin, Alan Wee-Chung Liew, Ming Jin, Shirui Pan
The paper introduces a deep learning-based signal quality assessment model that differentiates clean from noisy ambulatory ECG recordings. It is trained on the Copenhagen Center for Health Technology-Contextualized Arrhythmia Database, the first ambulatory ECG database that includes both physical and patient-reported contextual data. The model maintains stable performance across other datasets such as MIT and PhysioNet/CinC Challenge 2021, and the study demonstrates how the model can be used to investigate complex ECG noise in conjunction with contextual information.
By Xiaopeng Mao, Marike Weisbjerg, Sadasivan Puthusserypady
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
The paper introduces DCGCNet, a dual-codebook graph collaborative network that jointly reconstructs ECG signals and classifies atrial fibrillation. It incorporates a local‑global contrastive module for noise‑invariant feature learning and an adaptive codebook vector quantizer to prevent codebook collapse. The model achieves state‑of‑the‑art intra‑dataset performance and consistently attains AUC > 0.98 across seven cross‑dataset settings, even under realistic noisy conditions.
By Hongtao Li, Jia Wei, Guoyao Li, Yuchen Lei, Guangnian Ma, Jia Xiao, Yuanjun Lai, Shuzhen Lv, Xueqiang Ouyang
The study investigates ECG biometrics by training a Siamese ResNet with late multi-lead fusion on a large dataset from cardiopulmonary exercise tests. It evaluates the model under realistic conditions, including exercise-induced stress and cross-session variability, achieving an intra-session rest-to-peak EER of 1.7% and a state‑of‑the‑art 3.9% on the CYBHi dataset. The results demonstrate that an intrinsic cardiac signature remains robust to physiological and temporal drift.
By Luca Thiebaud (AMU, AMU SCI, DIAPRO, LIS), Paul Chauchat (AMU SCI, AMU, LIS, DIAPRO), Mustapha Ouladsine (AMU SCI, AMU, LIS, DIAPRO), St\'ephane Delliaux (AMU, APHM, C2VN)
The paper introduces Graph-CMMC, a graph-based pseudo‑multimodal contrastive learning framework for 12‑lead ECG representations. It transforms ECG waveforms into Gramian Angular Difference Field (GADF) images to create complementary views, then aligns these views while modeling inter‑lead dependencies with a graph module. Experiments on coronary artery occlusion classification show that Graph-CMMC performs competitively with supervised methods, highlighting the value of GADF representations and explicit graph modeling for robust ECG analysis.
By Mengyu Wang, Kozo Okada, Takafumi Goto, Natsuko Jinba, Hiroki Yamaya, Kiyoshi Hibi, Tomoki Hamagami
arXiv:2607. 23406v1 Announce Type: cross Abstract: Continuous cuffless blood pressure (BP) monitoring is essential for connected health systems and wearable devices, enabling early detection, longitudinal tracking, and personalized management of cardiovascular disease.
By Bo Wu, Haoling Wang, Zhuodiao Kuang, Kateryna Shapovalenko
The study investigates whether ECG representations derived from a β-variational autoencoder (VAE) can distinguish patients with myocardial scar (LGE+) from those without (LGE-) using routine ECG data. In a cohort of 300 cardiomyopathic patients, the β-VAE achieved an AUC of 0.577 (sensitivity 0.775) with Gradient Boosting, while the foundation ECGx.AI model reached an AUC of 0.686 with Random Forest. Dynamic Time Warping reconstruction errors differed significantly between classes in most leads and improved classification to an AUC of 0.643 with Logistic Regression, suggesting these errors could serve as markers of scar-related ECG changes.
By Shayan Sharifi, Riccardo Treu, Ilaria Gandin, Federico Garoia, Marco Merlo, Giulia Cisotto
BeatGraph is a self‑supervised model that represents infant ECG recordings as graphs of individual heartbeats rather than fixed‑length patches, allowing it to capture the higher heart rates and distinct waveform patterns of infants. The model uses a shared beat encoder, a Transformer for temporal ordering, and graph attention layers to produce a window embedding, which is pretrained on a large unlabeled infant ECG corpus and fine‑tuned for tasks such as sleep‑wake detection, infant‑state classification, activity‑source identification, and affect recognition. BeatGraph achieves state‑of‑the‑art performance on multiple infant‑specific benchmarks and transfers well to pediatric and adult ECG datasets, while also releasing the first public infant ECG corpus collected in diverse home and classroom settings.
By Mohammad Nur Hossain Khan, M. S. Krafczyk, Beverly G. Bolster, Nancy McElwain, Mark A. Hasegawa-Johnson, Bashima Islam
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...