Papers with Code 아카이브

Sleep Quality

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 8
평가표: 1
보존된 평가 기록: 1

<span style="color:grey; opacity: 0.6">( Image credit: DeepSleep )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Dreem-Organization/RobustSleepNet

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RobustSleepNet: Transfer learning for automated sleep staging at scale

https://github.com/Dreem-Organization/dreem-learning-evaluation

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Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

https://github.com/Dreem-Organization/dreem-learning-open

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Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

https://github.com/HuayuLiArizona/Conformalized-Multiple-Instance-Learning-For-MedTS

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Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized Models

https://github.com/IoBT-VISTEC/MetaSleepLearner

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MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

https://github.com/Jeongseungwoo/Efficient-Continuous-Manifold-Learning

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Deep Efficient Continuous Manifold Learning for Time Series Modeling

https://github.com/Mithunjha/EarEEG_KnowledgeDistillation

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A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

https://github.com/Niloy333/eegFloss

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eegFloss: A Python package for refining sleep EEG recordings using machine learning models

https://github.com/Raminghorbanii/Self-Supervised-PPG-Representation-Learning-Shows-High-Inter-Subject-Variability

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Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

https://github.com/SajadMo/SleepEEGNet

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SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

https://github.com/TDA-Jyamiti/zzgril

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Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data

https://github.com/acrophase/sleep_staging_kd

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A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging

https://github.com/ai4healthuol/s4sleep

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S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models

https://github.com/akaraspt/deepsleepnet

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DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

https://github.com/arlenejohn/CNN_sleep_apnea

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A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors

https://github.com/arlenejohn/Sleep_apnea_SpO2

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SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches

https://github.com/biomedical-signal-processing/sleepyland

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SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models

https://github.com/cruiseresearchgroup/zzzgpt-acl-2024-

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ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

https://github.com/dlcjfgmlnasa/NeuroNet

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NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

https://github.com/dlcjfgmlnasa/SynthSleepNet

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Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework

https://github.com/dslab-uniud/OSAS

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AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning

https://github.com/dslaborg/frequency-pretraining

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Data-Efficient Sleep Staging with Synthetic Time Series Pretraining

https://github.com/dslaborg/sumo

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Advanced sleep spindle identification with neural networks

https://github.com/dslaborg/sumov2

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From Sleep Staging to Spindle Detection: Evaluating End-to-End Automated Sleep Analysis

https://github.com/ecgresearch/heartbert

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HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis

https://github.com/emadeldeen24/ADAST

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ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

https://github.com/gianlucatruda/quantified-sleep

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Quantified Sleep: Machine learning techniques for observational n-of-1 studies

https://github.com/gianpaolopalo13/in_ear_eeg_vs_psg

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Comparison analysis between standard polysomnographic data and in-ear-EEG signals: A preliminary study

https://github.com/gist-ailab/IITNet-official

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Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG

https://github.com/gist-ailab/sleepyco

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SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning

https://github.com/hsd1503/DL-ECG-Review

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Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

https://github.com/jathurshan0330/cross-modal-transformer

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Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers

https://github.com/jukiecheung/mvitime

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Contrastive Learning for Sleep Staging based on Inter Subject Correlation

https://github.com/komdec/detectsleepnet

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An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNet

https://github.com/konkoniknik/Noisy-Labels-EPD

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Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise

https://github.com/konkoniknik/am-stimuli

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Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.