medical-1

Sleep Quality

3260 papers • 126 benchmarks • 313 datasets

( Image credit: DeepSleep )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in sleep-quality-1

Trend

Dataset

Best Model

Actions

100 sleep nights of 8 caregivers

Libraries

i

Use these libraries to find sleep-quality-1 models and implementations

Datasets

No datasets available.

Subtasks

Sleep Stage Detection Spindle Detection Sleep apnea detection Sleep Micro-event detection Sleep Micro-event detection

Most implemented papers

ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

Marwah Alaofi, Yonchanok Khaokaew, Thuc Hanh Nguyen, Kaixin Ji, H. Kegalle•Mon Oct 23 2023

This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge two-stage framework that harnesses the power of Large Language Models to deliver precise sleep predictions paired with actionable feedback, addressing the limitations of existing solutions.

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Content

Multimodal Sleep Stage Detection

Sleep Arousal Detection

K-complex detection

Sleep Quality Prediction

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DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal

Alexandre Gramfort, Valentin Thorey, E. Mignot, Stanislas Chambon, P. Arnal•Thu Dec 06 2018

A novel deep learning architecture called Dreem One Shot Event Detector (DOSED) is proposed, inspired by object detectors developed for computer vision such as YOLO and SSD, which predicts locations, durations and types of events in EEG time series.

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DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Y. Guan, Hongyang Li•Wed Aug 28 2019

DeepSleep is presented, which ranked first in the 2018 PhysioNet Challenge for automatically segmenting sleep arousal regions based on polysomnographic recordings, and features accurate, high-resolution, and fast delineation of sleep arousals.

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

G. Truda•Thu May 13 2021

This paper applies statistical learning techniques to an observational Quantified-Self (QS) study to build a descriptive model of sleep quality, demonstrating that an observational study can greatly narrow down the number of features that need to be considered when designing interventional QS studies.

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An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

Zhenghua Chen, Cuntai Guan, Chengyu Liu, Emadeldeen Eldele, Min Wu, C. Kwoh, Xiaoli Li•Tue Apr 27 2021

A novel attention-based deep learning architecture called AttnSleep is proposed to classify sleep stages using single channel EEG signals to outperforms state-of-the-art techniques in terms of different evaluation metrics.

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MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor

Win-Ken Beh, Yi-Hsuan Wu, An-Yeu (Andy) Wu•Tue Nov 02 2021

The result of data analysis show the potential uses of the recorded modalities and the feasibility of the MW elicitation protocol, and a reproducible baseline system as a preliminary benchmark is presented.

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Advanced sleep spindle identification with neural networks

S. Bialonski, L. Kaulen, J. Schwabedal, Jules Schneider, P. Ritter•Sat Feb 05 2022

A U-Net-type deep neural network model is presented that exceeds that of the state-of-the-art detector and of most experts in the MODA dataset and shows improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably.

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DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

R. Fonod•Mon Feb 28 2022

Results suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable in DeepSleep 2.0.

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A Knowledge Distillation Framework for Enhancing Ear-EEG Based Sleep Staging with Scalp-EEG Data

M. Anandakumar, Jathurshan Pradeepkumar, S. L. Kappel, Chamira U. S. Edussooriya, A. D. Silva•Wed Oct 26 2022

This work proposes a cross-modal knowledge distillation strategy that enhances the accuracy of the ear-EEG based sleep staging and can be applied to a wide range of deep learning models.

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

Gianpaolo Palo, Luigi Fiorillo, G. Monachino, Michal Bechny, M. Melnykowycz, Athina Tzovara, Valentina Agostini, F. Faraci•Sun Dec 31 2023

In-ear-EEG is a valuable solution for home-based sleep monitoring, however further studies with a larger and more heterogeneous dataset are needed.

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