Data Processing Tools - EEG/ECG
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https://physionet.org/content/apdet/1.0.0/
The apdet software package uses a new automated method to diagnose and quantify obstructive sleep apnea from single-lead electrocardiograms based on detection of the periodic oscillations in cardiac interbeat intervals that are often associated with prolonged cycles of sleep apnea.
Before building or using the apdet software, you must install the free WFDB software package. Under MS-Windows, you will also need to install the free Cygwin environment before building or using the apdet software.
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https://pypi.org/project/yasa/
A Python package to analyze polysomnographic sleep recordings
Automatic sleep staging of polysomnography data (see preprint article).
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https://orbi.uliege.be/handle/2268/188955
To detect artifacts and arousals in a reliable, systematic and reproducible automatic way, we developed an automatic detection based on time and frequency analysis with adapted thresholds derived from data themselves. The automatic detection performance is assessed using 5 statistic parameters, on 60 whole night sleep recordings coming from 35 healthy volunteers (male and female) aged between 19 and 26.
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Click: Resources
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https://github.com/anasimtiaz/sleep-edfx-toolbox
Free and open-source software package designed specifically for the analysis of sleep-related signals stored in the European Data Format (EDF).
It provides functionality for reading, processing, and visualizing PSG data.
Also includes modules for sleep stage scoring and sleep fragmentation analysis
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https://github.com/Mensen/swa-matlab
Sleep Wave Analysis - an open source toolbox for matlab to score and analyse various waveforms in sleep EEG data
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https://wonambi-python.github.io/
A Python package for the analysis of EEG, ECoG and other electrophysiology modalities. It allows for visualization of the data and sleep stage scoring in a GUI. This package provides automatic detectors for spindles and slow waves.
Data Processing Tools - Actigraphy
Research/clinical and consumer-grade motion-based and multi-sensor wearables devices
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https://pypi.org/project/pyActigraphy/
Open-source python package for actigraphy and light exposure data visualization and analysis.
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Sleep Tracker Menu. Zenodo doi: https://doi.org/10.5281/zenodo.7369861
Call to action: an open-source pipeline for standardized performance evaluation of sleep-tracking technology. Sleep. 2023;46(2). doi: 10.1093/sleep/zsac304. PubMed PMID: 36611112
Analytical pipeline and functions for testing the performance of sleep-tracking technology. Zenodo doi: http://doi.org/10.5281/zenodo.3762086
Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls. Chronobiol Int. 2021;38(7):1010-22. doi: 10.1080/07420528.2021.1903481. PubMed PMID: 33792456; PMCID: PMC8255273.
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Associated publication: Menghini L, Yuksel D, Prouty D, Baker FC, King C, de Zambotti M. Wearable and mobile technology to characterize daily patterns of sleep, stress, presleep worry, and mood in adolescent insomnia. Sleep Health. 2023;9(1):108-16. Epub 20221224. doi: 10.1016/j.sleh.2022.11.006. PubMed PMID: 36567194.
R-code: Supplementary appendices. Zenodo doi: http://doi.org/10.5281/zenodo.7507246
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The goal of snoozr is to provide researchers with a toolkit for R to aid in the processing, description, and modeling of sleep data collected by FitBit trackers.
SNOOZR Link: https://github.com/trackthatsleep/snoozr