Sleep Quality

13 papers with code • 1 benchmarks • 0 datasets

( Image credit: DeepSleep )

Most implemented papers

ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

cruiseresearchgroup/zzzgpt-acl-2024- 24 Oct 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 (LLMs).

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

Dreem-Organization/dosed 7 Dec 2018

The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD.

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

emadeldeen24/AttnSleep 28 Apr 2021

The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.

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

gianlucatruda/quantified-sleep 14 May 2021

This paper applies statistical learning techniques to an observational Quantified-Self (QS) study to build a descriptive model of sleep quality.

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

rickwu11/MAUS_dataset_baseline_system 3 Nov 2021

Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github. com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71. 6 %, 66. 7 %, and 59. 9 % in ECG, fingertip PPG, wristband PPG, respectively.

Advanced sleep spindle identification with neural networks

dslaborg/sumo Scientific Reports 2022

Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset.

DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

rfonod/deepsleep2 AI 2022

DeepSleep 2. 0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge.

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

gianpaolopalo13/in_ear_eeg_vs_psg 18 Jan 2024

On average, we demonstrate a high similarity between PSG and in-ear-EEG signals in terms of JSD-FSI (0. 79 +/- 0. 06 -awake, 0. 77 +/- 0. 07 -NREM, and 0. 67 +/- 0. 10 -REM) and in line with the similarity values computed independently on standard PSG-channel-combinations.