Papers Sleep Staging
“Sleep Staging” 태그가 달린 논문 116편 · 필터 해제
eegFloss: A Python package for refining sleep EEG recordings using machine learning models
Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their roles in cognitive processes. EEG is a cornerstone in sleep research, s…
EEGSleep StagingSLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models
Despite advances in deep learning for automatic sleep staging, clinical adoption remains limited due to challenges in fair model evaluation, generalization across diverse datasets, model bias, and variability in human an…
EEGSleep StagingFrom Sleep Staging to Spindle Detection: Evaluating End-to-End Automated Sleep Analysis
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater inco…
Privacy PreservingSleep StagingSpindle DetectionPSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning
Polysomnography (PSG) signals are essential for studying sleep processes and diagnosing sleep disorders. Analyzing PSG data through deep neural networks (DNNs) for automated sleep monitoring has become increasingly feasi…
Contrastive LearningSelf-Supervised LearningSleep StagingPSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging
Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and recording devices, such as sleep data. While…
Domain AdaptationSleep StagingVision Transformer Accelerator ASIC for Real-Time, Low-Power Sleep Staging
This paper introduces a lightweight vision transformer aimed at automatic sleep staging in a wearable device. The model is trained on the MASS SS3 dataset and achieves an accuracy of 82.9% on a 4-stage classification tas…
Sleep StagingSleepGMUformer: A gated multimodal temporal neural network for sleep staging
Sleep staging is a key method for assessing sleep quality and diagnosing sleep disorders. However, current deep learning methods face challenges: 1) postfusion techniques ignore the varying contributions of different mod…
EEGSleep QualitySleep StagingMultimodal Sleep Stage and Sleep Apnea Classification Using Vision Transformer: A Multitask Explainable Learning Approach
Sleep is an essential component of human physiology, contributing significantly to overall health and quality of life. Accurate sleep staging and disorder detection are crucial for assessing sleep quality. Studies in the…
ClassificationSleep QualitySleep StagingsDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging
Automatic sleep staging based on electroencephalography (EEG) and electromyography (EMG) signals is an important aspect of sleep-related research. Current sleep staging methods suffer from two major drawbacks. First, the…
EEGElectromyography (EMG)Sleep StagingEnhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized Models
Large language models (LLMs) exhibit remarkable capabilities in visual inspection of medical time-series data, achieving proficiency comparable to human clinicians. However, their broad scope limits domain-specific preci…
Arrhythmia DetectionConformal PredictionDecision MakingMultiple Instance Learning+3Fusion of Millimeter-wave Radar and Pulse Oximeter Data for Low-burden Diagnosis of Obstructive Sleep Apnea-Hypopnea Syndrome
Objective: The aim of the study is to develop a novel method for improved diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical or home settings, with the focus on achieving diagnostic performance co…
DiagnosticSleep StagingTemporal LocalizationWorld of ScoreCraft: Novel Multi Scorer Experiment on the Impact of a Decision Support System in Sleep Staging
Manual scoring of polysomnography (PSG) is a time intensive task, prone to inter scorer variability that can impact diagnostic reliability. This study investigates the integration of decision support systems (DSS) into P…
DiagnosticSleep StagingSelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment
In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, and recording conditions can lead to doma…
EEGSleep StagingTransfer LearningMamba-based Deep Learning Approaches for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
Study Objectives: We investigate Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non-intrusive dual-module wireless wearable system measuring chest electroc…
EEGElectrocardiography (ECG)MambaPhotoplethysmography (PPG)+1Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves
Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep studies. Recent work aims to leverage a…
Sleep StagingTopological Data AnalysisBiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
In this paper, we address the challenges in automatic sleep stage classification, particularly the high computational cost, inadequate modeling of bidirectional temporal dependencies, and class imbalance issues faced by …
Automatic Sleep Stage ClassificationClassificationEEGFeature Importance+2SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms
Sleep monitoring plays a crucial role in maintaining good health, with sleep staging serving as an essential metric in the monitoring process. Traditional methods, utilizing medical sensors like EEG and ECG, can be effec…
Data AugmentationEEGSleep StagingZero-Shot LearningSPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without label…
Brain Computer InterfaceDomain AdaptationEEGEEG based sleep staging+2Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications
Accurate sleep stage classification is crucial for diagnosing sleep disorders and evaluating sleep quality. While polysomnography (PSG) remains the gold standard, photoplethysmography (PPG) is more practical due to its a…
Photoplethysmography (PPG)Sleep QualitySleep StagingDeep Learning-based Automated Diagnosis of Obstructive Sleep Apnea and Sleep Stage Classification in Children Using Millimeter-wave Radar and Pulse Oximeter
Study Objectives: To evaluate the agreement between the millimeter-wave radar-based device and polysomnography (PSG) in diagnosis of obstructive sleep apnea (OSA) and classification of sleep stage in children. Methods: 2…
ClassificationSleep StagingSpecificity