Literature DB >> 16929927

Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.

Jennifer Caffarel1, G John Gibson, J Phil Harrison, Clive J Griffiths, Michael J Drinnan.   

Abstract

We have compared sleep staging by an automated neural network (ANN) system, BioSleep (Oxford BioSignals) and a human scorer using the Rechtschaffen and Kales scoring system. Sleep study recordings from 114 patients with suspected obstructed sleep apnoea syndrome (OSA) were analysed by ANN and by a blinded human scorer. We also examined human scorer reliability by calculating the agreement between the index scorer and a second independent blinded scorer for 28 of the 114 studies. For each study, we built contingency tables on an epoch-by-epoch (30 s epochs) comparison basis. From these, we derived kappa (kappa) coefficients for different combinations of sleep stages. The overall agreement of automatic and manual scoring for the 114 studies for the classification {wake / light-sleep / deep-sleep / REM} was poor (median kappa = 0.305) and only a little better (kappa = 0.449) for the crude {wake / sleep} distinction. For the subgroup of 28 randomly selected studies, the overall agreement of automatic and manual scoring was again relatively low (kappa = 0.331 for {wake light-sleep / deep-sleep REM} and kappa = 0.505 for {wake / sleep}), whereas inter-scorer reliability was higher (kappa = -0.641 for {wake / light-sleep / deep-sleep / REM} and kappa = 0.737 for {wake / sleep}). We conclude that such an ANN-based analysis system is not sufficiently accurate for sleep study analyses using the R&K classification system.

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Year:  2006        PMID: 16929927     DOI: 10.1007/s11517-005-0002-4

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  10 in total

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7.  Issues in computerized polysomnography.

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9.  Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings.

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Journal:  Med Biol Eng Comput       Date:  2002-07       Impact factor: 2.602

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  10 in total
  8 in total

1.  Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification.

Authors:  A S Muthanantha Murugavel; S Ramakrishnan
Journal:  Med Biol Eng Comput       Date:  2015-08-22       Impact factor: 2.602

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Review 3.  A review of signals used in sleep analysis.

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4.  Fully parametric sleep staging compatible with the classical criteria.

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5.  Determination of sleep stage separation ability of features extracted from EEG signals using principle component analysis.

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6.  Automatic Sleep Staging in Patients With Obstructive Sleep Apnea Using Single-Channel Frontal EEG.

Authors:  Pei-Lin Lee; Yi-Hao Huang; Po-Chen Lin; Yu-An Chiao; Jen-Wen Hou; Hsiang-Wen Liu; Ya-Ling Huang; Yu-Ting Liu; Tzi-Dar Chiueh
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7.  Detection of compressed tracheal sound patterns with large amplitude variation during sleep.

Authors:  A Kulkas; E Rauhala; E Huupponen; J Virkkala; M Tenhunen; A Saastamoinen; S-L Himanen
Journal:  Med Biol Eng Comput       Date:  2008-02-21       Impact factor: 2.602

8.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals.

Authors:  Christian Berthomier; Xavier Drouot; Maria Herman-Stoïca; Pierre Berthomier; Jacques Prado; Djibril Bokar-Thire; Odile Benoit; Jérémie Mattout; Marie-Pia d'Ortho
Journal:  Sleep       Date:  2007-11       Impact factor: 5.849

  8 in total

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