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.
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.
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arXiv Computer Vision
Sep 22
X-Beat: An Explainable Framework for ECG Image Classification
arXiv:2609.22631v1 Announce Type: new Abstract: Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction...
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
Sep 7
A Hybrid Predictive Ensemble of Machine Learning and Deep Neural Networks for Early Cardiovascular Disease Risk Assessment
The paper presents a hybrid predictive ensemble that merges machine learning and deep neural network techniques to detect and prognosticate cardiovascular disease early. It processes real‑time physiological data from IoMT devices, applying preprocessing, feature selection, and optimized classifiers (SVM, Random Forest, XGBoost) within an ensemble architecture. The cloud‑based system achieves higher accuracy, fewer false positives, and consistent performance on real‑world datasets, supporting continuous patient monitoring and clinical decision support.
arXiv Machine Learning
Sep 14
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
The paper presents a new labelled ICU dataset and benchmarks for detecting atrial fibrillation (AF) from electrocardiograms (ECGs). It compares three AI approaches—feature‑based classifiers, deep learning, and ECG foundation models—across Canadian ICU data and the 2021 PhysioNet challenge, finding that ECG foundation models with transfer learning achieve the highest F1 score (0.89). The study demonstrates the feasibility of automated AF monitoring in ICU settings and provides resources for further research.
arXiv AI
Jun 11
Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification
arXiv:2606. 12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment.
arXiv AI
Sep 25
AI-based detection of worsening heart failure from low-resolution telemonitoring data
The study introduces TRACER, a Transformer-based model that uses contrastive event representation to predict timelines leading to heart failure hospitalizations from low-resolution, irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings, contrastive pre‑training for anomaly detection, and independent binary classifiers, and was evaluated on biomarker sequences from 276 heart failure patients. The model achieved 66.7% accuracy in predicting hospitalization timelines with a 7.9% overestimation, outperforming other tested models by reformulating training as an event detection problem.
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...
arXiv AI
Jun 8
MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection
arXiv:2606. 06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes.
arXiv Machine Learning
Sep 15
Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models
The paper evaluates deep learning models for electrocardiogram‑based emotion recognition, focusing on generalization across datasets rather than dataset‑specific performance. It introduces two open‑source tools—ARRC for standardized benchmarking and ARDT for inter‑dataset training—to merge three public AER datasets (CUADS, ASCERTAIN, DREAMER) into a more variable benchmark. Using these tools, the authors compare three prominent deep learning architectures and two CNN baselines with hyperparameter tuning and 10‑fold cross‑validation, revealing trade‑offs between accuracy and model complexity and providing a reproducible benchmark for future research.
arXiv AI
Jun 2
A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
arXiv Machine Learning
Jul 2
A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data
arXiv:2607. 01145v1 Announce Type: new Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.