arXiv Machine Learning August 31, 2026 By Israel Campero Jurado, Zoe Bousraou, Lara Benning, Sara Padilla Neira, Alexander Breuss, Robert Riener, Esther Irene Schwarz, Elisabeth Wilhelm

Spectral Features Dominate BCG Respiratory-Event Detection: A Large-Scale Patient-Independent Comparison of Feature Groups in Sleep Apnea Patients

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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

Jul 20

Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals

arXiv:2607. 15477v1 Announce Type: new Abstract: Sleep apnea diagnosis via polysomnography remains resource intensive and relies on time consuming manual data analysis and scoring.

By Shashank Manjunath, Mukesh Cheemakurthi, Aarti Sathyanarayana

llms computer-vision

Hugging Face Trending Papers

Jun 22

Deep learning-based detection of cessation of breathing in pre-term infants

Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate and oxygen saturation thresholds, often generating high false-positive alarm rates and missing short or irregular events.

multimodal

arXiv Machine Learning

Jun 26

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

arXiv:2606. 26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states.

By Andrei Velichko, Mehmet Tahir Huyut

arXiv AI

Jun 9

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.

By Jonathan F. Carter, Lionel Tarassenko

llms rag efficiency multimodal benchmarks

arXiv Machine Learning

4d ago

SemPSG: A Semantic Channel-Aware Foundation Model for Polysomnography Analysis

arXiv:2609.36619v1 Announce Type: new Abstract: Polysomnography (PSG) integrates multiple physiological signals to provide a comprehensive characterization of human sleep, yet its heterogeneous chann...

By Junyu Chen, Chenxi Liu, Shiqin Tang, Hao Miao, Wanyun Ling, Ziyue Li, Hongbin Liu, Gaofeng Meng

One email a morning, machine-written

Related stories

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

Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals

Deep learning-based detection of cessation of breathing in pre-term infants

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

SemPSG: A Semantic Channel-Aware Foundation Model for Polysomnography Analysis