Literature DB >> 8174288

Automatic sleep-spindle detection procedure: aspects of reliability and validity.

P Schimicek1, J Zeitlhofer, P Anderer, B Saletu.   

Abstract

The paper describes a reliable and valid method for the automatic detection of sleep spindles in whole night polygraphy. The recording of a multi-channel EEG during sleep polysomnography was performed in 10 healthy volunteers aged 20-35 years. This objective method should improve the time-consuming and subjective visual evaluation by increasing the accuracy and allowing the calculation of quantitative variables (i.e., frequency and amplitude), thereby facilitating scientific work with quantitative data. An important part of the method is the treatment of artifacts (i.e., muscle and spindle-like alpha activity). Compared to hardware solutions, our software method has the advantage of higher flexibility in regard to artifact identification and usual spindle definitions.

Mesh:

Year:  1994        PMID: 8174288     DOI: 10.1177/155005949402500108

Source DB:  PubMed          Journal:  Clin Electroencephalogr        ISSN: 0009-9155


  29 in total

1.  Autoassociative MLP in sleep spindle detection.

Authors:  E Huupponen; A Värri; S L Himanen; J Hasan; M Lehtokangas; J Saarinen
Journal:  J Med Syst       Date:  2000-06       Impact factor: 4.460

2.  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

3.  No Associations between Interindividual Differences in Sleep Parameters and Episodic Memory Consolidation.

Authors:  Sandra Ackermann; Francina Hartmann; Andreas Papassotiropoulos; Dominique J-F de Quervain; Björn Rasch
Journal:  Sleep       Date:  2015-06-01       Impact factor: 5.849

Review 4.  [Sleep spindles-Function, detection and use as biomarker for diagnostics in psychiatry].

Authors:  Jules Schneider; Justus T C Schwabedal; Stephan Bialonski
Journal:  Nervenarzt       Date:  2022-06-08       Impact factor: 1.297

5.  Instrumental conditioning of human sensorimotor rhythm (12-15 Hz) and its impact on sleep as well as declarative learning.

Authors:  Kerstin Hoedlmoser; Thomas Pecherstorfer; Georg Gruber; Peter Anderer; Michael Doppelmayr; Wolfgang Klimesch; Manuel Schabus
Journal:  Sleep       Date:  2008-10       Impact factor: 5.849

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.  Interindividual sleep spindle differences and their relation to learning-related enhancements.

Authors:  Manuel Schabus; Kerstin Hoedlmoser; Thomas Pecherstorfer; Peter Anderer; Georg Gruber; Silvia Parapatics; Cornelia Sauter; Gerhard Kloesch; Wolfgang Klimesch; Bernd Saletu; Josef Zeitlhofer
Journal:  Brain Res       Date:  2007-11-28       Impact factor: 3.252

10.  The significance of sigma neurofeedback training on sleep spindles and aspects of declarative memory.

Authors:  I Berner; M Schabus; T Wienerroither; W Klimesch
Journal:  Appl Psychophysiol Biofeedback       Date:  2006-07-15
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