arXiv Machine Learning

Vib2ECG: A Co-Located Chest-Lead SCG-ECG Dataset and Benchmark for ECG Reconstruction

Vib2ECG introduces a new dataset of synchronized twelve‑lead ECG and chest‑wall vibration recordings collected at the six standard precordial sites (V1–V6) from 17 subjects, using inertial measurement units. The accompanying benchmark demonstrates that a lightweight 364K‑parameter U‑Net can reconstruct electrical cardiac signals at various locations from these low‑cost vibrational signals, though a hallucination effect—where ECG waveforms appear without corresponding electrical activity—was observed and analyzed. This work expands the use of cardiac vibrational signals for mobile ECG monitoring and offers insights into the spatial relationship between cardiac electrical and mechanical activities.

arXiv AI

Sep 21

Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

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)

benchmarks

arXiv Computation and Language

1d ago

ARTA: Graph Combinatorial Optimization for Robust Interbeat Interval Estimation from Wearable Electrocardiogram Signals

ARTA is a graph‑based combinatorial framework that improves interbeat interval estimation from wearable ECG by integrating candidates from multiple detectors into a directed acyclic graph. It uses detector agreement, ECG quality, and history‑dependent transition costs to select a coherent heartbeat sequence via beam‑search dynamic programming, effectively suppressing false detections and recovering missed beats. On benchmark datasets, ARTA reduces heart‑rate mean absolute error by 54% compared to the best single detector and achieves high accuracy in R‑peak detection and IBI estimation while remaining lightweight and training‑free.

By Shovito Barua Soumma, Asiful Arefeen, Hassan Ghasemzadeh

safety

arXiv AI

Aug 11

CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals

arXiv:2608. 07759v1 Announce Type: cross Abstract: Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clinical deterioration.

By Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire, Kosisochukwu Ogbuanya, Sanaa Reeves, Sandy Akoy

multimodal

arXiv AI

Jul 28

Blood Pressure Estimation from PPG: A Comparative Study of Direct and ECG-Mediated Deep Learning Pipelines

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

benchmarks

arXiv Machine Learning

Oct 8

BeatFlow-ECG: Rectified Flow for ECG Reconstruction from Indirect Wearable Signals

BeatFlow-ECG is a conditional rectified‑flow model that reconstructs single‑channel ECG signals from synchronized photoplethysmography (PPG) and inertial measurement unit (IMU) data. The architecture uses a convolutional encoder‑decoder with a transformer bottleneck and explicit flow‑time conditioning, incorporating motion information through IMU‑derived features, motion‑dependent loss weighting, and a curriculum learning strategy. Evaluated on PPG‑DaLiA and WESAD datasets with leave‑one‑subject‑out protocols, BeatFlow‑ECG outperforms deterministic, adversarial, and diffusion‑based baselines, achieving Pearson correlations of 0.983–0.986 and R‑peak F1 scores of 0.946–0.955, while reducing L1 error compared to Conditional DDPM‑1D.

By Mohamed Kamel, Sahar Selim, Walaa Medhat, Tamer Nadeem

llms diffusion safety

Hugging Face Trending Papers

Oct 6

BeatFlow-ECG: Rectified Flow for ECG Reconstruction from Indirect Wearable Signals

Continuous cardiac monitoring outside clinical settings requires signals that are both informative and practical to collect during daily life. Electrocardiography (ECG) provides rich information about...

llms diffusion safety

arXiv AI

Oct 21

ECG for laboratory values:A comprehensive physiological and technical analysis

arXiv:2610.28507v1 Announce Type: cross Abstract: Laboratory measurements are essential for clinical decision making but typically require invasive blood sampling. Recent research suggests that ECG r...

By Walentin Palanchuk

multimodal

arXiv AI

Jun 2

Motif-based morphology signatures for interpretable ECG screening and monitoring

arXiv:2606. 00107v1 Announce Type: cross Abstract: Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic.

By Nivedita Bijlani, Mauricio Villarroel

safety

arXiv Machine Learning

Sep 22

Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis

arXiv:2609.22179v1 Announce Type: cross Abstract: Stress detection using physiological signals has gained significant attention due to its impact on both physical and mental health. While existing ap...

By Zhanna Balyan, Sachin Kumar

safety

arXiv AI

Aug 18

P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval

arXiv:2608. 14656v1 Announce Type: cross Abstract: Photoplethysmography (PPG) is widely used in consumer wearables because of its low cost and ease of acquisition.

By Zhongli Wu, Zhuangzhi Gao, He Zhao, Feixiang Zhou, Fu Wang, Jinru Ding, Yuankai Wang, Hongyi Qin, Gregory Y. H. Lip, Bil Kirmani, Yalin Zheng

rag

arXiv Machine Learning

Sep 1

Beat-Synchronous Tokenization for ECG Transformers

arXiv:2608.30367v1 Announce Type: new Abstract: Transformer-based electrocardiogram (ECG) models commonly tokenize waveforms into fixed temporal patches. Though convenient, fixed patching can split h...

By Ahmed Sameh, Nolan Wilson, Max Enderlein, Yogatheesan Varatharajah

llms nlp

arXiv Machine Learning

Aug 14

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

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