arXiv AI August 14, 2026 By Seyyed Ali Hoseini, Javad Baseri, Hamid Saadatfar, Edris Hoseini Gol, AmirHossein Eshghi

Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts

arXiv:2608. 12446v1 Announce Type: cross Abstract: Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models against a single reference hypnogram despite known inter-scorer variability.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 7

Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets

arXiv:2607. 04934v1 Announce Type: new Abstract: Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring.

By Zihao Wei, Yulin Gong, Yudan Lv

benchmarks

arXiv AI

Jun 8

SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal

arXiv:2606. 07351v1 Announce Type: cross Abstract: Classification of sleep stages is one of the most important diagnostic approaches for a variety of sleep-related disorders.

By Rafsan Jany, Md. Hamjajul Ashmafee, Iqram Hussain, Md Azam Hossain

arXiv AI

Jun 3

SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model

arXiv:2603. 26738v3 Announce Type: replace-cross Abstract: While automated sleep staging has achieved expert-level accuracy, its clinical adoption is hindered by a lack of auditable reasoning.

By Guifeng Deng, Pan Wang, Mengfan Niu, Jiquan Wang, Shuying Rao, Junyi Xie, Xi'ang Chen, Sha Zhao, Gang Pan, Wanjun Guo, Tao Li, Haiteng Jiang

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 15

AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts

arXiv:2512. 14461v2 Announce Type: replace Abstract: Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care.

By Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon, Stephan Bialonski

benchmarks

Hugging Face Trending Papers

Jul 6

EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection

Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy. We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in scalp EEG.

llms agents safety

arXiv AI

Aug 6

SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging

arXiv:2603. 26738v4 Announce Type: replace-cross Abstract: Sleep staging is essential for sleep assessment and disorder diagnosis.

By Guifeng Deng, Pan Wang, Mengfan Niu, Jiquan Wang, Shuying Rao, Junyi Xie, Xi'ang Chen, Sha Zhao, Gang Pan, Wanjun Guo, Tao Li, Haiteng Jiang

llms fine-tuning multimodal benchmarks safety

Related stories

Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets

SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal

SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model

AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts

EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection

SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging