An E-health solution for automatic sleep classification according to Rechtschaffen and Kales:: Validation study of the Somnolyzer 24 x 7 utilizing the Siesta database

被引:217
作者
Anderer, P
Gruber, G
Parapatics, S
Woertz, M
Miazhynskaia, T
Klösch, G
Saletu, B
Zeitlhofer, J
Barbanoj, MJ
Danker-Hopfe, H
Himanen, SL
Kemp, B
Penzel, T
Grözinger, M
Kunz, D
Rappelsberger, P
Schlögl, A
Dorffner, G
机构
[1] Med Univ Vienna, Dept Psychiat, Vienna, Austria
[2] Siesta Grp Schlafanal GmbH, Vienna, Austria
[3] Austrian Res Inst Artificial Intelligence, Vienna, Austria
[4] Med Univ Vienna, Dept Neurol, Vienna, Austria
[5] Univ Barcelona, Hosp Sant Pau, Area Invest Farmacol, Barcelona, Spain
[6] Univ Hosp Benjamin Franklin, Dept Psychiat, Berlin, Germany
[7] Tampere Univ Hosp, Tampere, Finland
[8] Westeinde Ziekenhuis, Sleep Ctr, The Hague, Netherlands
[9] Univ Marburg, Med Policlin, Marburg, Germany
[10] St Valentinushaus, Kiedrich, Germany
[11] Dept Psychiat & Psychotherapy, Berlin, Germany
[12] Med Univ Vienna, Brain Res Inst, Vienna, Austria
[13] Univ Technol, Inst Human Comp Interfaces, Graz, Austria
[14] Med Univ Vienna, Dept Med Cybernet & Artificial Intelligence, Vienna, Austria
关键词
automatic sleep stager; polysomnography; expert system; Cohen's kappa; pattern detection algorithm; feature extraction algorithm;
D O I
10.1159/000085205
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 x 7(TM)) consists of a raw data quality check, a feature extraction algorithm ( density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report(TM)). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20-95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen's kappa: 0.72) between the Somnolyzer 24 x 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen's kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 x 7 analyses ( including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen's kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 x 7 and demonstrated its applicability in clinical routine and sleep studies. Copyright (C) 2005 S. Karger AG, Basel.
引用
收藏
页码:115 / 133
页数:19
相关论文
共 57 条
[1]  
*ACCR COMM, STAND ACCR SLEEP DIS
[2]   Temporal evolution of coherence and power in the human sleep electroencephalogram [J].
Achermann, P ;
Borbely, AA .
JOURNAL OF SLEEP RESEARCH, 1998, 7 :36-41
[3]   Computer-assisted sleep staging [J].
Agarwal, R ;
Gotman, J .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2001, 48 (12) :1412-1423
[4]   Artifact processing in computerized analysis of sleep EEG -: A review [J].
Anderer, P ;
Roberts, S ;
Schlögl, A ;
Gruber, G ;
Klösch, G ;
Herrmann, W ;
Rappelsberger, P ;
Filz, O ;
Barbanoj, MJ ;
Dorffner, G ;
Saletu, B .
NEUROPSYCHOBIOLOGY, 1999, 40 (03) :150-157
[5]  
ANDERER P, 2002, THESIS VIENNA
[6]  
Anderer P, 2004, ESSENTIALS APPL EEG, V1, P307, DOI [DOI 10.1146/ANNUREV.ECOLSYS.34.011802.132351, 10.1146/annurev.ecolsys.34.011802.132351]
[7]   Sleep laboratory study on single and repeated dose effects of paroxetine, alprazolam and their combination in healthy young volunteers [J].
Barbanoj, MJ ;
Clos, S ;
Romero, S ;
Morte, A ;
Giménez, S ;
Lorenzo, JL ;
Luque, A ;
Dal-Ré, R .
NEUROPSYCHOBIOLOGY, 2005, 51 (03) :134-147
[8]   On the use of neural network techniques to analyze sleep EEG data - Third communication: Robustification of the classificator by applying an algorithm obtained from 9 different networks [J].
Baumgart-Schmitt, R ;
Herrmann, WM ;
Eilers, R .
NEUROPSYCHOBIOLOGY, 1998, 37 (01) :49-58
[9]   Muscle artifacts in the sleep EEG: Automated detection and effect on all-night EEG power spectra [J].
Brunner, DP ;
Vasko, RC ;
Detka, CS ;
Monahan, JP ;
Reynolds, CF ;
Kupfer, DJ .
JOURNAL OF SLEEP RESEARCH, 1996, 5 (03) :155-164
[10]  
Carskadon M.A., 2000, PRINCIPLES PRACTICE, P15, DOI 10.1016/B0-72-160797-7/50009-4