arXiv Computer Vision
ECGWmark: Deep Watermarking of Electrocardiogram Signals With Error-Correcting Codes
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arXiv Computer Vision
Oct 12
ECGWmark: Deep Watermarking for Electrocardiogram Signals
ECGWmark is a fully convolutional, quantization‑free deep watermarking framework designed for electrocardiogram (ECG) signals. It embeds patient metadata into ECGs while preserving high‑frequency diagnostic details by operating in a continuous latent space and using a localized frame‑level mechanism to resist severe temporal desynchronization. A differentiable Physiological Loss guided by a clinical classifier helps maintain diagnostic semantics, achieving over 90% channel‑bit extraction accuracy under circular phase shifts and demonstrating mixed effects on class‑level diagnostic metrics in the MIT‑BIH benchmark.
arXiv AI
Jul 14
ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification
arXiv:2607. 09680v1 Announce Type: cross Abstract: Continuous cardiac monitoring in wearable devices demands classifiers that are simultaneously accurate, energy-efficient, and deployable on resource-constrained hardware.
arXiv Machine Learning
Jul 28
Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction
arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
arXiv Machine Learning
Jun 2
ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
arXiv:2606. 02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems.
Hugging Face Trending Papers
Sep 23
From ECG Signals to Representative-Morphology Heatmaps for Biometric Recognition
Electrocardiography (ECG) contains subject-specific morphology that supports biometric recognition, yet image-based performance depends on how the waveform is rendered. We introduce representative-mor...
arXiv Computer Vision
Sep 24
From ECG Signals to Representative-Morphology Heatmaps for Biometric Recognition
The paper introduces representative‑morphology heatmaps, a deterministic ECG‑to‑image representation that averages the five beats closest to the block mean within each ten‑beat block, producing either a conventional trace or a dense cardiac‑time‑by‑lead heatmap. Experiments on PTB, ECG‑ID, and MIMIC‑IV‑ECG‑DEMO show that heatmaps consistently improve verification and identification performance across 15 compact models, reducing EER by an average of 9.59 percentage points and increasing Rank‑1 by 24.69 points. The study also demonstrates that ImageNet initialization benefits multilead datasets, that performance does not scale monotonically with model size, and that useful channel combinations vary by cohort and biometric task.