NanoSleep is a compact hybrid temporal convolutional network designed for single‑channel EEG sleep stage classification. It integrates a learnable Sinc‑convolutional front end, dual‑branch feature extraction for multi‑scale temporal and spectral representations, a gated dilated temporal convolutional backbone with channel recalibration, and a conditional random field for sequence‑level decoding. Evaluated on Sleep‑EDF and Sleep‑EDF‑Expanded datasets, NanoSleep consistently outperforms six baseline methods and demonstrates that each major component contributes to its performance.
By S M Asif Hossain, Shruti Kshirsagar
LightSleepX is a lightweight, inception‑based dual‑modal network designed for sleep staging in resource‑constrained environments. It uses depthwise separable convolutions, multi‑scale enhanced attention for efficient EEG/EOG feature extraction, and a Mamba encoder for long‑range temporal modeling. On public benchmarks, it achieves 85.9% accuracy on Sleep‑EDF‑20 and 81.8% on ISRUC‑S3 with only 0.049M parameters and 195.9 MFLOPs.
By Yi Wang
arXiv:2508. 11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia.
By Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi
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
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
arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.
By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka
arXiv:2602. 07628v2 Announce Type: replace Abstract: While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to task-specific models that focus on localized micro-structure features.
By Keondo Park, Younghoon Na, Yourim Choi, Hyunwoo Ryu, Hyun-Woo Shin, Hyung-Sin Kim
BrainCodes is a lightweight EEG foundation model that uses a quantization-based neural tokenizer and a spatiotemporal encoder to learn transferable EEG features from over 800 hours of data across more than 1,000 participants. The model is pretrained with a self‑supervised objective that encourages spatial connectivity and sensitivity to narrowband neural oscillations, enabling it to match or surpass state‑of‑the‑art performance on tasks such as sleep staging, attention assessment, and cognitive load estimation. It achieves these results while using up to 97% fewer trainable parameters, making it suitable for low‑resource wearable devices, and its evaluation framework analyzes the geometry and invariances of the learned representations.
By Kleanthis Avramidis, Ivan Tashev, Dimitra Emmanouilidou
arXiv:2607. 23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics.
By Juntang Wang, Yihan Wang, Hao Wu, Jiayu Gao, Shixin Xu, Dongmian Zou
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
arXiv:2606. 19888v1 Announce Type: cross Abstract: Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data.
By Feng Wu, Harsh Deep, Eric Lehman, Sanyam Kapoor, Guoshuai Zhao, Rahul Krishnan, Gari Clifford, Li-wei H Lehman
AF-Mamba is a deep learning model that predicts atrial fibrillation (AF) onset one hour in advance using long‑term RR intervals. It combines temporal convolutional networks for local feature extraction with Mamba, a state‑space model for long‑range sequence modeling, achieving high sensitivity (0.889) and specificity (0.943) in subject‑wise testing. The model maintains strong performance across unseen datasets, offering a favorable trade‑off between predictive accuracy and computational efficiency for real‑time ambulatory monitoring.
By Yongbin Lee, Ki H. Chon