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.
arXiv:2606. 10972v1 Announce Type: cross Abstract: This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning.
By Ipek Sen, Ozgur Ozdemir, Elena Battini Sonmez
This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning. In pulmonary sound classification, spectrogram-based representations suffer from inconsistent temporal dimensions due to varying respiratory cycle durations.
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
The paper presents a Time-Aware transformer-based model designed to predict Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) using only respiratory data from daily-use ventilators. By capturing symptom patterns and their temporal progression, the model generates meaningful patient representations and outperforms traditional methods across multiple classification tasks. This approach aims to provide timely detection of AECOPD while minimizing latency associated with clinical and laboratory data.
By Weihao Qu, Ling Zheng, Dongyang Wang, Jiacun Wang, Haowen Pan
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
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
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: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
DualStabSleepNet (DSSNet) is a dual-domain diffusion stabilization network designed to improve the robustness of automatic sleep staging across heterogeneous recording conditions. It employs a continuous-scale diffusion-based module to suppress noise while preserving physiological signals, then transforms stabilized signals into time-frequency representations for a Vision Transformer backbone. A teacher‑student guided diffusion feature stabilization further reduces feature drift, achieving state‑of‑the‑art accuracy on four public PSG datasets and demonstrating strong performance under cross‑dataset distribution shifts.
By Chongjian Wang, Chen Liu, Junjie Gao, Xiaofang Zhong, Shiyuan Han, Tong Zhang
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.