Literature DB >> 23366866

Validation of a novel automatic sleep spindle detector with high performance during sleep in middle aged subjects.

Sabrina L Wendt1, Julie A E Christensen, Jacob Kempfner, Helle L Leonthin, Poul Jennum, Helge B D Sorensen.   

Abstract

Many of the automatic sleep spindle detectors currently used to analyze sleep EEG are either validated on young subjects or not validated thoroughly. The purpose of this study is to develop and validate a fast and reliable sleep spindle detector with high performance in middle aged subjects. An automatic sleep spindle detector using a bandpass filtering approach and a time varying threshold was developed. The validation was done on sleep epochs from EEG recordings with manually scored sleep spindles from 13 healthy subjects with a mean age of 57.9 ± 9.7 years. The sleep spindle detector reached a mean sensitivity of 84.6 % and a mean specificity of 95.3 %. The sleep spindle detector can be used to obtain measures of spindle count and density together with quantitative measures such as the mean spindle frequency, mean spindle amplitude, and mean spindle duration.

Mesh:

Year:  2012        PMID: 23366866     DOI: 10.1109/EMBC.2012.6346905

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


  12 in total

1.  Spatio-temporal structure of sleep slow oscillations on the electrode manifold and its relation to spindles.

Authors:  Paola Malerba; Lauren N Whitehurst; Stephen B Simons; Sara C Mednick
Journal:  Sleep       Date:  2019-01-01       Impact factor: 5.849

2.  Minimizing Interrater Variability in Staging Sleep by Use of Computer-Derived Features.

Authors:  Magdy Younes; Patrick J Hanly
Journal:  J Clin Sleep Med       Date:  2016-10-15       Impact factor: 4.062

3.  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

4.  A Novel Sleep Stage Scoring System: Combining Expert-Based Rules with a Decision Tree Classifier.

Authors:  Kristin M Gunnarsdottir; Charlene E Gamaldo; Rachel M E Salas; Joshua B Ewen; Richard P Allen; Sridevi V Sarma
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

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.  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

7.  Identifying Sleep Biomarkers to Evaluate Cognition in HIV.

Authors:  Hilda Azimi; Kristin M Gunnarsdottir; Sridevi V Sarma; Alyssa A Gamaldo; Rachel M E Salas; Charlene E Gamaldo
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2020-07

8.  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

9.  Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing.

Authors:  Athanasios Tsanas; Gari D Clifford
Journal:  Front Hum Neurosci       Date:  2015-04-08       Impact factor: 3.169

Review 10.  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

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