arXiv:2606. 24974v1 Announce Type: new Abstract: Explainability techniques are used to assess the output of various deep learning models.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 24035v1 Announce Type: cross Abstract: Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful patterns, yet explanations that appear plausible for individual predictions may systematically misrepresent model behavior.
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.
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: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: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.
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient p...