arXiv Machine Learning

Wolff-Parkinson-White Detection at 471:1 Class Imbalance: A Leakage-Controlled Study of the Data Bottleneck

arXiv:2608. 14633v1 Announce Type: cross Abstract: Wolff-Parkinson-White (WPW) syndrome is a congenital cardiac pre-excitation, clinically important and often missed on the resting 12-lead ECG.

arXiv Machine Learning

Jun 24

Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics

arXiv:2606. 22054v2 Announce Type: replace-cross Abstract: Detectors for GNSS radio-frequency impairments (jamming, spoofing, multipath) are usually reported with a single AUC measured on the distribution they were tuned on.

By Chakshu Baweja

arXiv AI

Jul 7

ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment

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

llms

arXiv AI

1d ago

When Single-Dataset Conclusions Fail: A 45-Task Study of Threshold Tuning and Resampling for Imbalanced Classification

arXiv:2608. 16147v1 Announce Type: new Abstract: Class-imbalance handling is routinely evaluated on a single benchmark dataset, and the resulting conclusions are reported as if they were properties of the method.

By Diyorbek Musaev

benchmarks

arXiv Machine Learning

Jul 7

Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection

arXiv:2607. 03009v1 Announce Type: new Abstract: Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial intelligence.

By Beatrice Zanchi, Giuliana Monachino, Alvise Dei Rossi, Luigi Fiorillo, Georgia Sarquella-Brugada, Giulio Conte, Francesca Dalia Faraci

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 28

Beyond Local Inspection: Global, Guideline-Grounded Evaluation of Post-hoc XAI Methods for ECG Classification

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.

By Nils Gumpfer, Michael Guckert, Samuel Sossalla, Birgit A{\ss}mus, Jennifer Hannig

computer-vision

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.

By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner

benchmarks

arXiv AI

Jul 16

The Entanglement Wall: Activation-Space Probes as Risk Detectors, Not Context Adjudicators

arXiv:2607. 13075v1 Announce Type: cross Abstract: Context can change whether a request is harmful without changing its topic or surface form.

By Dominik Schwarz

arXiv AI

Jul 23

Decodable but Not Detectable: A Leakage Fingerprint for Near-OOD Benchmarks

arXiv:2607. 19393v1 Announce Type: cross Abstract: While auditing a perturbation-based OOD detector on a document benchmark, we recorded an AUROC of 0.

By Vishnu Bindu Balachandran

rag benchmarks

arXiv Machine Learning

22h ago

Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

arXiv:2608. 17775v1 Announce Type: new Abstract: Anomaly detectors are hardest to deploy exactly where training data is scarcest: a newly commissioned production line has a handful of verified "golden" samples and no machine-learning engineer on the factory floor.

By Ayusha Abbas, Saram Abbas, Kabita Adhikari

arXiv Machine Learning

Aug 4

Automated ECG Interval Measurement and Wave Delineation Using Fast Fourier Convolution ResNet

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

computer-vision safety

arXiv Machine Learning

Jun 16

False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Control

arXiv:2606. 15153v1 Announce Type: new Abstract: Selective prediction with distribution-free risk control promises that, with confidence 1-delta over the calibration draw, the error rate of accepted inputs stays below a user budget alpha.

By Jingwen Zhou, Mingzhe Wang

safety

arXiv AI

Aug 5

FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection

arXiv:2608. 03597v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.

By Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk, Vahid Abolghasemi, Naser Hossein Motlagh, Sadasivan Puthusserypady, Daniel Teichmann, Abdolrahman Peimankar

rag benchmarks

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