arXiv AI July 31, 2026 By Mathieu Cherpitel, Janne Luijten, Thomas B\"ack, Camiel Verhamme, Martijn Tannemaat, Anna V. Kononova

How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

Read the original on arXiv AI →

arXiv:2601. 10191v2 Announce Type: replace Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis.

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 AI.

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 Machine Learning

Jun 10

A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection

arXiv:2606. 10410v1 Announce Type: new Abstract: Objective: Accurate classification of physiological signals in real-world deployments is challenged by sensor noise, motion artifacts, and distribution shifts between training and deployment data.

By Davood Fattahi, Runze Yan, Saurabh Kataria, Zhaoliang Chen, Xiao Hu

llms

Hugging Face Trending Papers

Aug 24

Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency

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...

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.

By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed

benchmarks

arXiv Machine Learning

Jun 24

Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition

arXiv:2601. 04181v2 Announce Type: replace Abstract: Reliable long-term decoding of gestures from surface electromyography (EMG) is hindered by signal drift caused by electrode displacement, muscle fatigue, and/or posture changes.

By Nia Touko, Matthew O A Ellis, Cristiano Capone, Alessio Burrello, Elisa Donati, Luca Manneschi

safety

arXiv Machine Learning

Sep 14

Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment

Prism‑SQA is a neural framework that interprets surface electromyography (sEMG) quality assessment by separating each signal into a clean component and five contaminant-specific components using a U‑Net with bidirectional LSTM. Each contaminant is verified against canonical physiological signatures, allowing clinicians to see how specific noises affect quality. The resulting quality indices can be customized for different clinical contexts without retraining, and Prism‑SQA matches or surpasses current black‑box methods on both continuous and binary quality tasks.

By Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao

safety

One email a morning, machine-written

Related stories

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection

Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition

Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment