"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data

被引:2
作者
Ray, L. B. [1 ]
Baena, D. [1 ,2 ]
Fogel, S. M. [1 ,2 ,3 ]
机构
[1] Univ Ottawa, Sch Psychol, Ottawa, ON K1N 6N5, Canada
[2] Univ Ottawa, Inst Mental Hlth Res Royal, Sleep Unit, Ottawa, ON K1Z 7K4, Canada
[3] Univ Ottawa, Brain & Mind Res Inst, Ottawa, ON K1H 8M5, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Polysomnography; Electroencephalography; Event marking; Signal processing; Automatic detection; Sleep architecture; Hypnogram; Scoring; Open-source; Graphical user interface; SLEEP SPINDLES; SLOW WAVES; ELECTROENCEPHALOGRAM;
D O I
10.1016/j.jneumeth.2024.110162
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. New method: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. Results: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, rereferencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. Comparison with existing methods: Counting Sheep PSG was built on the foundation created by sleepSMG (https ://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. Conclusions: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.
引用
收藏
页数:12
相关论文
共 34 条
[1]  
Aggarwal K, 2018, IEEE INT CONF BIG DA, P1318, DOI 10.1109/BigData.2018.8622286
[2]   Maintaining vs. enhancing motor sequence memories: Respective roles of striatal and hippocampal systems [J].
Albouy, Genevieve ;
Fogel, Stuart ;
King, Bradley R. ;
Laventure, Samuel ;
Benali, Habib ;
Karni, Avi ;
Carrier, Julie ;
Robertson, Edwin M. ;
Doyon, Julien .
NEUROIMAGE, 2015, 108 :423-434
[3]  
[Anonymous], 1968, Brain information service
[4]   Functional differences in cerebral activation between slow wave-coupled and uncoupled sleep spindles [J].
Baena, Daniel ;
Fang, Zhuo ;
Gibbings, Aaron ;
Smith, Dylan ;
Ray, Laura B. ;
Doyon, Julien ;
Owen, Adrian M. ;
Fogel, Stuart M. .
FRONTIERS IN NEUROSCIENCE, 2023, 16
[5]   Brain activations time locked to slow wave-coupled sleep spindles correlates with intellectual abilities [J].
Baena, Daniel ;
Fang, Zhuo ;
Ray, Laura B. ;
Owen, Adrian M. ;
Fogel, Stuart M. .
CEREBRAL CORTEX, 2023, 33 (09) :5409-5419
[6]   Electroencephalogram in humans [J].
Berger, H .
ARCHIV FUR PSYCHIATRIE UND NERVENKRANKHEITEN, 1929, 87 :527-570
[7]   Slow oscillations in human non-rapid eye movement sleep electroencephalogram: effects of increased sleep pressure [J].
Bersagliere, Alessia ;
Achermann, Peter .
JOURNAL OF SLEEP RESEARCH, 2010, 19 (01) :228-237
[8]  
Biswal S, 2017, Arxiv, DOI [arXiv:1707.08262, DOI 10.48550/ARXIV.1707.08262]
[9]   A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series [J].
Chambon, Stanislas ;
Galtier, Mathieu N. ;
Arnal, Pierrick J. ;
Wainrib, Gilles ;
Gramfort, Alexandre .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2018, 26 (04) :758-769
[10]   Sleep: An Open-Source Python']Python Software for Visualization, Analysis, and Staging of Sleep Data [J].
Combrisson, Etienne ;
Vallat, Raphael ;
Eichenlaub, Jean-Baptiste ;
O'Reilly, Christian ;
Lajnef, Tarek ;
Guillot, Aymeric ;
Ruby, Perrine M. ;
Jerbi, Karim .
FRONTIERS IN NEUROINFORMATICS, 2017, 11