Literature DB >> 17947058

Automatic sleep spindle detection in patients with sleep disorders.

S Devuyst1, T Dutoit, J F Didier, F Meers, E Stanus, P Stenuit, M Kerkhofs.   

Abstract

In this paper, we present a new automatic method for sleep spindle detection. It consist of a generalisation of the Schimicek's method that takes more types of artefacts into account and uses variable thresholds regarding the statistical properties of the signal. Validity of our process is examined on the basis of visual spindle scoring performed by an expert. Results obtained are compared to those obtained by Schimicek's method. For a specificity of 90%, we obtain a sensitivity of 76.9% while Schimicek's method has a sensitivity of 70.4%. Moreover an increase of the area under the ROC curve is observed and confirms that the detection process is improved.

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Year:  2006        PMID: 17947058     DOI: 10.1109/IEMBS.2006.259298

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


  6 in total

1.  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

2.  Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods.

Authors:  Simon C Warby; Sabrina L Wendt; Peter Welinder; Emil G S Munk; Oscar Carrillo; Helge B D Sorensen; Poul Jennum; Paul E Peppard; Pietro Perona; Emmanuel Mignot
Journal:  Nat Methods       Date:  2014-02-23       Impact factor: 28.547

3.  A comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequencies.

Authors:  Péter Przemyslaw Ujma; Ferenc Gombos; Lisa Genzel; Boris Nikolai Konrad; Péter Simor; Axel Steiger; Martin Dresler; Róbert Bódizs
Journal:  Front Hum Neurosci       Date:  2015-02-17       Impact factor: 3.169

Review 4.  Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods.

Authors:  Dorothée Coppieters 't Wallant; Pierre Maquet; Christophe Phillips
Journal:  Neural Plast       Date:  2016-07-11       Impact factor: 3.599

5.  Sleep spindle detection based on non-experts: A validation study.

Authors:  Rui Zhao; Jinbo Sun; Xinxin Zhang; Huanju Wu; Peng Liu; Xuejuan Yang; Wei Qin
Journal:  PLoS One       Date:  2017-05-11       Impact factor: 3.240

6.  Advanced sleep spindle identification with neural networks.

Authors:  Lars Kaulen; Justus T C Schwabedal; Jules Schneider; Philipp Ritter; Stephan Bialonski
Journal:  Sci Rep       Date:  2022-05-10       Impact factor: 4.996

  6 in total

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