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 combining multi‑scale temporal and spectral representations, a gated dilated temporal convolutional backbone with channel recalibration, and a conditional random field for sequence decoding. Evaluated on Sleep‑EDF datasets, NanoSleep consistently outperforms six baseline methods, with ablation studies confirming the contribution of each component.
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
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: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
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
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: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
LiteEMG-FM is an efficient hybrid CNN‑Transformer foundation model designed for electromyography (EMG) sensing. It is pretrained on 16 diverse upper‑ and lower‑limb EMG datasets, enabling representations that generalize across users and datasets. The model incorporates a lightweight, always‑on 1D‑CNN wake‑up module that filters rest and non‑target activity, activating LiteEMG‑FM only for valid gestures, and is evaluated in full inference offloading, split inference, and full on‑device processing scenarios, showing superior performance over state‑of‑the‑art time‑series foundation models and supervised baselines, especially in zero‑calibration cross‑participant and data‑scarce conditions.
By Tianhao Wu, Xu Wu, Amirmohammad Radmehr, Jiawei Yu, Yi Wu, Phuc Nguyen, Jian Liu
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
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