Literature DB >> 24909981

Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research.

Christian O'Reilly1,2, Nadia Gosselin1,3, Julie Carrier1,3, Tore Nielsen1,2.   

Abstract

Manual processing of sleep recordings is extremely time-consuming. Efforts to automate this process have shown promising results, but automatic systems are generally evaluated on private databases, not allowing accurate cross-validation with other systems. In lacking a common benchmark, the relative performances of different systems are not compared easily and advances are compromised. To address this fundamental methodological impediment to sleep study, we propose an open-access database of polysomnographic biosignals. To build this database, whole-night recordings from 200 participants [97 males (aged 42.9 ± 19.8 years) and 103 females (aged 38.3 ± 18.9 years); age range: 18-76 years] were pooled from eight different research protocols performed in three different hospital-based sleep laboratories. All recordings feature a sampling frequency of 256 Hz and an electroencephalography (EEG) montage of 4-20 channels plus standard electro-oculography (EOG), electromyography (EMG), electrocardiography (ECG) and respiratory signals. Access to the database can be obtained through the Montreal Archive of Sleep Studies (MASS) website (http://www.ceams-carsm.ca/en/MASS), and requires only affiliation with a research institution and prior approval by the applicant's local ethical review board. Providing the research community with access to this free and open sleep database is expected to facilitate the development and cross-validation of sleep analysis automation systems. It is also expected that such a shared resource will be a catalyst for cross-centre collaborations on difficult topics such as improving inter-rater agreement on sleep stage scoring.
© 2014 European Sleep Research Society.

Entities:  

Keywords:  collaboration tool; methodology; open-data; reproducibility

Mesh:

Year:  2014        PMID: 24909981     DOI: 10.1111/jsr.12169

Source DB:  PubMed          Journal:  J Sleep Res        ISSN: 0962-1105            Impact factor:   3.981


  31 in total

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Authors:  Huy Phan; Fernando Andreotti; Navin Cooray; Oliver Y Chen; Maarten De Vos
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3.  Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning.

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4.  Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.

Authors:  Maurice Abou Jaoude; Haoqi Sun; Kyle R Pellerin; Milena Pavlova; Rani A Sarkis; Sydney S Cash; M Brandon Westover; Alice D Lam
Journal:  Sleep       Date:  2020-11-12       Impact factor: 5.849

5.  A deep learning approach for real-time detection of sleep spindles.

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Journal:  J Neural Eng       Date:  2019-02-21       Impact factor: 5.379

6.  Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

Authors:  Huy Phan; Fernando Andreotti; Navin Cooray; Oliver Y Chen; Maarten De Vos
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7.  Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms.

Authors:  Min-Yin Liu; Adam Huang; Norden E Huang
Journal:  Front Hum Neurosci       Date:  2017-05-18       Impact factor: 3.169

8.  Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors.

Authors:  Navin Cooray; Fernando Andreotti; Christine Lo; Mkael Symmonds; Michele T M Hu; Maarten De Vos
Journal:  Clin Neurophysiol       Date:  2021-02-03       Impact factor: 3.708

9.  A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.

Authors:  Dechun Zhao; Renpin Jiang; Mingyang Feng; Jiaxin Yang; Yi Wang; Xiaorong Hou; Xing Wang
Journal:  Technol Health Care       Date:  2022       Impact factor: 1.205

10.  Using a quadratic parameter sinusoid model to characterize the structure of EEG sleep spindles.

Authors:  Abdul J Palliyali; Mohammad N Ahmed; Beena Ahmed
Journal:  Front Hum Neurosci       Date:  2015-05-05       Impact factor: 3.169

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