arXiv AI
Analysis of Respiratory Sinus Arrhythmia with Neural Networks
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arXiv AI
Sep 25
Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach
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.
arXiv AI
Jul 7
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
arXiv:2407. 20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments.
arXiv AI
Jun 19
Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI
arXiv:2601. 00014v2 Announce Type: replace-cross Abstract: Heart failure (HF) affects 11.
arXiv Machine Learning
Sep 10
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
arXiv:2609.08992v1 Announce Type: new Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework comb...
arXiv AI
Jun 10
Boosting ECG Classification Performance by Pre-training with Synthesized Data
arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.
arXiv AI
Sep 16
Decoder Design Matters for ECG Delineation
The paper introduces R‑U‑Net, an ECG delineation model that combines a ResNet‑18 encoder with a U‑Net decoder. It demonstrates that this decoder design outperforms a ResNet‑18 + fully convolutional network baseline across 16 in‑domain settings and improves cross‑domain performance by 8.1 mIoU. Ablation studies reveal that the decoder contributes more to performance gains than the evaluated semi‑supervised learning methods.