Literature DB >> 22254656

Automatic sleep spindles detection--overview and development of a standard proposal assessment method.

S Devuyst1, T Dutoit, P Stenuit, M Kerkhofs.   

Abstract

Since the 1970s, various automatic sleep spindles procedures have been implemented and presented in the literature. Unfortunately, their results are not easily comparable because the databases, the assessment methods and the terminologies employed are often radically different. In this study, we propose a systematic assessment method for any automatic sleep spindles detection algorithm. We apply this assessment method to our own automatic detection process in order to illustrate and legitimate its use. We obtain a global sensitivity of 70.20%, for a false positive proportion (relative to the total number of visually scored sleep spindles) of only 26.44% (False positive rate = 1.38% and specificity = 98.62%).

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Year:  2011        PMID: 22254656     DOI: 10.1109/IEMBS.2011.6090491

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  17 in total

1.  Inter-expert and intra-expert reliability in sleep spindle scoring.

Authors:  Sabrina L Wendt; Peter Welinder; Helge B D Sorensen; Paul E Peppard; Poul Jennum; Pietro Perona; Emmanuel Mignot; Simon C Warby
Journal:  Clin Neurophysiol       Date:  2014-11-10       Impact factor: 3.708

2.  Enhanced automated sleep spindle detection algorithm based on synchrosqueezing.

Authors:  Muammar M Kabir; Reza Tafreshi; Diane B Boivin; Naim Haddad
Journal:  Med Biol Eng Comput       Date:  2015-03-17       Impact factor: 2.602

3.  Fast and Stable Signal Deconvolution via Compressible State-Space Models.

Authors:  Abbas Kazemipour; Ji Liu; Krystyna Solarana; Daniel A Nagode; Patrick O Kanold; Min Wu; Behtash Babadi
Journal:  IEEE Trans Biomed Eng       Date:  2017-04-13       Impact factor: 4.538

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

Authors:  Prathamesh M Kulkarni; Zhengdong Xiao; Eric J Robinson; Apoorva Sagarwal Jami; Jianping Zhang; Haocheng Zhou; Simon E Henin; Anli A Liu; Ricardo S Osorio; Jing Wang; Zhe Chen
Journal:  J Neural Eng       Date:  2019-02-21       Impact factor: 5.379

5.  Delay differential analysis for dynamical sleep spindle detection.

Authors:  Aaron L Sampson; Claudia Lainscsek; Christopher E Gonzalez; István Ulbert; Orrin Devinsky; Dániel Fabó; Joseph R Madsen; Eric Halgren; Sydney S Cash; Terrence J Sejnowski
Journal:  J Neurosci Methods       Date:  2019-01-30       Impact factor: 2.390

6.  Identifying sleep spindles with multichannel EEG and classification optimization.

Authors:  Ning Mei; Michael D Grossberg; Kenneth Ng; Karen T Navarro; Timothy M Ellmore
Journal:  Comput Biol Med       Date:  2017-09-01       Impact factor: 4.589

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.  Spindles in Svarog: framework and software for parametrization of EEG transients.

Authors:  Piotr J Durka; Urszula Malinowska; Magdalena Zieleniewska; Christian O'Reilly; Piotr T Różański; Jarosław Żygierewicz
Journal:  Front Hum Neurosci       Date:  2015-05-08       Impact factor: 3.169

9.  Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools.

Authors:  Christian O'Reilly; Tore Nielsen
Journal:  Front Hum Neurosci       Date:  2015-06-24       Impact factor: 3.169

10.  Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis.

Authors:  Tarek Lajnef; Sahbi Chaibi; Jean-Baptiste Eichenlaub; Perrine M Ruby; Pierre-Emmanuel Aguera; Mounir Samet; Abdennaceur Kachouri; Karim Jerbi
Journal:  Front Hum Neurosci       Date:  2015-07-28       Impact factor: 3.169

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