Papers Automatic Sleep Stage Classification
“Automatic Sleep Stage Classification” 태그가 달린 논문 27편 · 필터 해제
NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification
Sleep stage classification from single-channel electroencephalography (EEG) is essential for wearable and home-based sleep monitoring. However, many deep learning models achieve high accuracy at the cost of large model s…
Automatic Sleep Stage ClassificationStaging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
Automated sleep staging is commonly approached as a supervised machine learning problem, with deep learning methods dominating recent research. While machine learning models achieve near-human level agreement with human-…
Automatic Sleep Stage ClassificationMSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification
Recent advancements in machine learning-based signal analysis, coupled with open data initiatives, have fuelled efforts in automatic sleep stage classification. Despite the proliferation of classification models, few hav…
Automatic Sleep Stage ClassificationBiT-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+2Evaluating sleep-stage classification: how age and early-late sleep affects classification performance
Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. …
Automatic Sleep Stage ClassificationClassificationSleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages
Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual het…
Automatic Sleep Stage ClassificationClassificationData AugmentationEEG+3Contrastive Learning for Sleep Staging based on Inter Subject Correlation
In recent years, multitudes of researches have applied deep learning to automatic sleep stage classification. Whereas actually, these works have paid less attention to the issue of cross-subject in sleep staging. At the …
Automatic Sleep Stage ClassificationContrastive LearningSleep StagingAutomatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals
The detection of human sleep stages is widely used in the diagnosis and intervention of neurological and psychiatric diseases. Some patients with deep brain stimulator implanted could have their neural activities recorde…
Automatic Sleep Stage ClassificationClassificationTransfer LearningA CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designe…
Automatic Sleep Stage ClassificationEEGElectroencephalogram (EEG)Toward 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+2A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick
With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods …
Automatic Sleep Stage ClassificationClassificationHeuristic SearchNeural Architecture SearchADAST: 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+2Time-Series Representation Learning via Temporal and Contextual Contrasting
Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and …
Automatic Sleep Stage ClassificationContrastive LearningEpilepsy PredictionFault Detection+7An 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+1RobustSleepNet: Transfer learning for automated sleep staging at scale
Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on t…
Automatic Sleep Stage ClassificationSleep StagingTransfer LearningAutomatic sleep stage classification with deep residual networks in a mixed-cohort setting
Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few smal…
Automatic Sleep Stage ClassificationBenchmarkingGeneral ClassificationGraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification
Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In…
Automatic Sleep Stage ClassificationClassificationEEGElectroencephalogram (EEG)+2End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep…
Automatic Sleep Stage ClassificationClassificationGeneral ClassificationSleep StagingMetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification con…
Automatic Sleep Stage ClassificationMeta-LearningSleep StagingTransfer Learning