Papers Sleep Stage Detection
“Sleep Stage Detection” 태그가 달린 논문 35편 · 필터 해제
The Breakthrough of Sleep: A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp
Sleep staging is essential for the assessment of sleep quality and the diagnosis of sleep-related disorders. Conventional polysomnography (PSG), while considered the gold standard, is intrusive, labor-intensive, and unsu…
Sleep Stage DetectionSleep QualityQuasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data
In this paper, we propose Quasi Zigzag Persistent Homology (QZPH) as a framework for analyzing time-varying data by integrating multiparameter persistence and zigzag persistence. To this end, we introduce a stable topolo…
Sleep Stage DetectionToward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinic…
Contrastive LearningDiagnosticEEGElectroencephalogram (EEG)+6MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
Sleep profoundly affects our health, and sleep deficiency or disorders can cause physical and mental problems. Despite significant findings from previous studies, challenges persist in optimizing deep learning models, es…
Contrastive LearningEEGSleep Stage DetectionHeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
The HeartBert model is introduced with three primary objectives: reducing the need for labeled data, minimizing computational resources, and simultaneously improving performance in machine learning systems that analyze E…
Heartbeat ClassificationSelf-Supervised LearningSleep Stage DetectionMulti-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks
Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, a…
EEGMulti-Task LearningSleep QualitySleep Stage DetectionNeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
The classification of sleep stages is a pivotal aspect of diagnosing sleep disorders and evaluating sleep quality. However, the conventional manual scoring process, conducted by clinicians, is time-consuming and prone to…
Contrastive LearningEEGElectroencephalogram (EEG)Mamba+2Structure-Preserving Transformers for Sequences of SPD Matrices
In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean g…
EEGEEG based sleep stagingSleep Stage DetectionSleep StagingCoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient's physiological recordings, could simplify the diagnostic process. Previous w…
DiagnosticEEGSleep Stage DetectionSleep Staging+1Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict di…
Activity RecognitionRepresentation LearningRhythmSelf-Supervised Learning+2SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and enables longitudinal sleep tracking in home environments. Conventionally, learning-based automatic sleep scoring on single-chann…
Contrastive LearningEEGElectroencephalogram (EEG)Sleep Stage DetectionToward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and in particular, deep-learning based algori…
Automatic Sleep Stage ClassificationDeep LearningGeneral ClassificationMultimodal Sleep Stage Detection+3Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, ofte…
Automatic Sleep Stage ClassificationBIG-bench Machine LearningDeep LearningMultimodal Sleep Stage Detection+2Using Ballistocardiography for Sleep Stage Classification
A practical way of detecting sleep stages has become more necessary as we begin to learn about the vast effects that sleep has on people's lives. The current methods of sleep stage detection are expensive, invasive to a …
ClassificationHeart Rate VariabilitySleep Stage DetectionAdaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
Unsupervised anomaly detection aims to build models to effectively detect unseen anomalies by only training on the normal data. Although previous reconstruction-based methods have made fruitful progress, their generaliza…
Anomaly DetectionDiversitySelf-Supervised LearningSleep Stage Detection+3A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress in this area, allowing for near-human a…
ECG based Sleep StagingEEGEEG based sleep stagingElectroencephalogram (EEG)+5ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a…
Automatic Sleep Stage ClassificationDomain AdaptationEEGEEG based sleep staging+2An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using …
Automatic Sleep Stage ClassificationEEGElectroencephalogram (EEG)Sleep Quality+1XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw…
Sleep Stage DetectionSleep StagingLearned Factor Graphs for Inference from Stationary Time Sequences
The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algo…
Sleep Stage Detection