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

• URL: http://arxiv.org/abs/2512.14461v1
• Date: Tue, 16 Dec 2025 14:49:11 GMT
• Title: AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
• Authors: Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon, Stephan Bialonski,
• Abstract summary: We present AnySleep, a deep neural network model that uses any electroencephalography (EEG) or electrooculography (EOG) data to score sleep at adjustable temporal resolutions.<n>The model attains state-of-the-art performance and surpasses or equals established baselines at 30-s epochs.
• Score: 1.7032702581423902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sleep is essential for good health throughout our lives, yet studying its dynamics requires manual sleep staging, a labor-intensive step in sleep research and clinical care. Across centers, polysomnography (PSG) recordings are traditionally scored in 30-s epochs for pragmatic, not physiological, reasons and can vary considerably in electrode count, montage, and subject characteristics. These constraints present challenges in conducting harmonized multi-center sleep studies and discovering novel, robust biomarkers on shorter timescales. Here, we present AnySleep, a deep neural network model that uses any electroencephalography (EEG) or electrooculography (EOG) data to score sleep at adjustable temporal resolutions. We trained and validated the model on over 19,000 overnight recordings from 21 datasets collected across multiple clinics, spanning nearly 200,000 hours of EEG and EOG data, to promote robust generalization across sites. The model attains state-of-the-art performance and surpasses or equals established baselines at 30-s epochs. Performance improves as more channels are provided, yet remains strong when EOG is absent or when only EOG or single EEG derivations (frontal, central, or occipital) are available. On sub-30-s timescales, the model captures short wake intrusions consistent with arousals and improves prediction of physiological characteristics (age, sex) and pathophysiological conditions (sleep apnea), relative to standard 30-s scoring. We make the model publicly available to facilitate large-scale studies with heterogeneous electrode setups and to accelerate the discovery of novel biomarkers in sleep.

Related papers

• Physiology as Language: Translating Respiration to Sleep EEG [43.02603129452492]
  This paper introduces a novel cross-physiology translation task: synthesizing sleep electroencephalography (EEG) from respiration signals.<n>We propose a waveform-conditional generative framework that preserves fine-grained respiratory dynamics while constraining the EEG target space through discrete tokenization.<n>Trained on over 28,000 individuals, our model achieves a 7% Mean Absolute Error in EEG spectrogram reconstruction.
  arXiv  Detail & Related papers  (2026-01-31T05:44:45Z)
• Exploring the Efficacy of Convolutional Neural Networks in Sleep Apnea Detection from Single Channel EEG [0.0]
  This paper presents a novel approach to the detection of sleep apnea using a Convolutional Neural Network (CNN) trained on single channel EEG data.<n>The proposed CNN achieved an accuracy of 85.1% and a Matthews Correlation Coefficient (MCC) of 0.22, demonstrating a significant potential for home based applications.
  arXiv  Detail & Related papers  (2025-08-16T04:58:00Z)
• Sleep Stage Classification using Multimodal Embedding Fusion from EOG and PSM [0.06282171844772422]
  This study introduces a novel approach that leverages ImageBind, a multimodal embedding deep learning model, to integrate PSM data with dual-channel EOG signals for sleep stage classification.<n>Our results demonstrate that fine-tuning ImageBind significantly improves classification accuracy, outperforming existing models.
  arXiv  Detail & Related papers  (2025-06-07T20:18:45Z)
• Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework [3.135883872525168]
  This study introduces SynthSleepNet, a multimodal hybrid self-supervised learning framework for analyzing polysomnography (PSG) data.<n> SynthSleepNet effectively integrates masked prediction and contrastive learning to leverage complementary features across multiple modalities.<n>It achieved superior performance compared to state-of-the-art methods across three downstream tasks.
  arXiv  Detail & Related papers  (2025-02-18T10:11:50Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [46.36100528165335]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study [43.28613210217385]
  We employ and compare three state-of-the-art generative models to generate PCG data.<n>Our results demonstrate that the generated PCG data closely resembles the original datasets.<n>In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs.
  arXiv  Detail & Related papers  (2024-12-17T18:07:40Z)
• Time-to-Event Pretraining for 3D Medical Imaging [44.46415168541444]
  We introduce time-to-event pretraining, a pretraining framework for 3D medical imaging models.<n>We use a dataset of 18,945 CT scans (4.2 million 2D images) and time-to-event distributions across thousands of EHR-derived tasks.<n>Our method improves outcome prediction, achieving an average AUROC increase of 23.7% and a 29.4% gain in Harrell's C-index across 8 benchmark tasks.
  arXiv  Detail & Related papers  (2024-11-14T11:08:54Z)
• Sleep Stage Classification Using a Pre-trained Deep Learning Model [0.0]
  "EEGMobile" is a machine-learning model that learns from electroencephalogram (EEG) spectrograms of brain signals. The model achieved an accuracy of 86.97% on a publicly available dataset named "Sleep-EDF20", outperforming other models proposed by different researchers.
  arXiv  Detail & Related papers  (2023-09-12T23:02:19Z)
• Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage [18.059360820527687]
  Sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy.
  arXiv  Detail & Related papers  (2023-02-17T07:37:54Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.