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
arXiv:2608. 00058v1 Announce Type: cross Abstract: Accurate measurement of ECG intervals, including PR, QRS duration, and QT/QTc, is central to cardiac diagnosis, yet the published ECG delineation literature evaluates performance almost exclusively as fiducial-point timing errors on small curated databases, rather than as clinical interval accuracy on large unselected cohorts.
By Farhan Adam Mukadam, Harshit Mishra, Nachiket Makwana, Pradyot Tiwari, Subramani Kandasamy, KVS Hari
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...
The study investigates how patient‑adaptive ECG lead‑channel allocation policies perform when evaluated by different diagnostic models. Two policies, ECG‑on‑Demand and MGA, trained with a simple logistic evaluator were tested on a more powerful ResNet1D evaluator, revealing that the adaptive advantage observed with the training evaluator disappears or reverses with the stronger evaluator. Across multiple budgets, policies, and metrics, all interactions favor fixed protocols under the strong evaluator, suggesting that adaptive channel selection must be jointly optimized with the diagnostic backbone.
By Xiaoyang Li, Zeyan Tao
arXiv:2609.23460v1 Announce Type: new
Abstract: Deep learning has advanced automated electrocardiogram (ECG) diagnosis, but the field's most accurate models, foundation models pretrained on millions...
By Shunbo Jia, Runze Ma, Haonan Lyu, Haijin Zhang, Qiang Yang, Caizhi Liao
arXiv:2608. 14723v1 Announce Type: cross Abstract: Reduced left ventricular ejection fraction (LVEF) is frequently asymptomatic and often detected only after advanced heart failure develops.
By Burcu Ozek, Aruna Mohan, David Vorchheimer, Daniel Weiss, Eyal Kedar, Tamar Sobol, Or Zilbershot, Fatemeh Afghah
The study evaluates how acquisition context and photoplethysmography (PPG) signals contribute to session‑calibrated blood pressure tracking using a wearable‑state model on 653 AuroraBP participants. Adding contextual information reduced mean absolute errors (MAEs) for systolic and diastolic BP by 0.436 mmHg and 0.126 mmHg, respectively, while PPG alone provided modest improvements. Exploratory follow‑ups with sensor‑history ensembles and pulse‑rate fusion yielded only slight gains, and a separate intraoperative model (VitalDB) showed limited PPG benefit.
By Farouk Ganiyu Adewumi, Timothy Oladunni
The paper introduces GRAFT-EEG, a method that removes the active‑set common‑reference gauge and transports each native EEG mask through a template motor leadfield, enabling target‑statistics‑blind transfer for cross‑dataset motor‑imagery decoding. Using a source‑fitted spectral classifier and a bounded state‑space‑model residual, the approach achieves higher balanced accuracy (60.865%) than a strict‑clean baseline (52.684%) across five leave‑one‑dataset‑out targets, and outperforms several control conditions in post‑hoc tests. The study also reports positive results in a separate 60‑participant same‑paradigm evaluation, though the dynamic‑static comparison was inconclusive.
"whyItMatters":"The method demonstrates that accounting for observation‑operator changes can substantially improve cross‑dataset EEG decoding performance, offering a more robust approach for motor‑imagery applications."
By Ying Zhou, Mengting Liu
arXiv:2607. 13738v5 Announce Type: replace-cross Abstract: Attribution maps for echocardiographic ejection-fraction models are evaluated by their overlap with an expert left-ventricular annotation, compared against a chance level that is computed from an area ratio rather than measured.
By Hyunkyung Han, Min Jung Kim
arXiv:2607. 05009v1 Announce Type: cross Abstract: Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short display formats, incomplete waveform digitization, lead loss, or signal corruption.
By Xiaocheng Fang, Haoyu Wang, Jieyi Cai, Qinghao Zhao, Jun Li, Shanwei Zhang, Guangkun Nie, Yujie Xiao, Shun Huang, Jiarui Jin, Hongmin Liu, Guodong Wang, Shuohua Chen, Liming Lin, Shouling Wu, Hongyan Li, Shenda Hong
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
arXiv:2607. 14613v1 Announce Type: cross Abstract: Long-tailed label distributions reduce the reliability of deep learning for electrocardiogram (ECG) arrhythmia diagnosis, particularly for clinically important but rare abnormalities.
By Jin Dai, Qiuzhen Zhang, Chenyun Dai, Danmei Lan, Can Han