Sleep Stage Scoring Using the Neural Network Model: Comparison Between Visual and Automatic Analysis in Normal Subjects and Patients

Schaltenbrand, Nicolas; Lengellé, Régis; Toussaint, Michel; Luthringer, R.; Carelli, Gabriella; Jacqmin, Anne; Lainey, Eric; Muzet, Alain; Mâcher, Jean-Paul

doi:10.1093/sleep/19.1.26

Cited by 139 publications

(97 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Characterization of sleep in terms of these discrete stages is a methodological concept that attempts to standardize analysis across reviewers and laboratories; it is not a biological fact [2]. The exact time of change of state is highly subjective and leaves room for interpretation by the scorer, who will score transitional epochs (e.g., Stage 1 and Stage 3) differently on different occasions [3]. Studies have shown interscorer agreement ranging from 67% to 91% [4]- [7] depending on different scoring epoch lengths and number of readers.…”

Section: Introductionmentioning

confidence: 99%

“…Ray et al [17] have developed an expert system with good performance on a limited data set. Schaltenbrand et al [3] presented a neural network model of automatic staging with results ranging from 84.5% for normal group to 81.0% for insomniac group using 30-s epochs. Gath and Bar-On [18] presented the feasibility of a method based on fuzzy clustering of variable length segments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computer-assisted sleep staging

Agarwal

Gotman

2001

IEEE Trans. Biomed. Eng.

194

101

View full text Add to dashboard Cite

Abstract-To address the subjectivity in manual scoring of polysomnograms, a computer-assisted sleep staging method is presented in this paper. The method uses the principles of segmentation and self-organization (clustering) based on primitive sleep-related features to find the pseudonatural stages present in the record. Sample epochs of these natural stages are presented to the user, who can classify them according to the Rechtschaffen and Kales (RK) or any other standard. The method then learns from these samples to complete the classification. This step allows the active participation of the operator in order to customize the staging to his/her preferences. The method was developed and tested using 12 records of varying types (normal, abnormal, male, female, varying age groups). Results showed an overall concurrence of 80.6% with manual scoring of 20-s epochs according to RK standard. The greatest amount of errors occurred in the identification of the highly transitional Stage 1, 54% of which was misclassified into neighboring stages 2 or Wake.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer-assisted sleep staging

Agarwal

Gotman

2001

IEEE Trans. Biomed. Eng.

194

101

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the various neural network-based systems reviewed were as efficient as non connectionist systems developed by other groups (Gaillard and Tissot, 1973;Smith et al, 1978;Ray et al, 1986;Itowi et al, 1990;Witting et al, 1996). Furthermore, performances of connectionist classifiers were often similar to those of experts (Mamelak et al, 1991;Robert et al, 1996;Schaltenbrand et al, 1996).…”

Section: Discussionmentioning

confidence: 93%

“…While no impairment of the artificial neural network classifier was observed by Mamelak et al (1991), Grö zinger et al (1995) and Schaltenbrand et al (1996) reported the need to alter their system. Although the systems developed by Roberts and Tarassenko (1992), Grö zinger et al (1995) and Robert et al (1996) mainly relied on a single EEG channel to establish an hypnogram, these systems are limited in that they can not be used in protocols which induce dissociation between EEG patterns and the behaviour of the subject.…”

Section: Discussionmentioning

confidence: 95%

“…In a second study (Schaltenbrand et al, 1996), the performance of the multilayer perceptron was tested on 20 healthy subject records and on 40 patient records (20 depressive patients and 20 insomniac patients treated with a benzodiazepine). Agreement between one visual scorer and the connectionist classification was 84.5% for healthy subject records, 81.5% for depressive and 81.0% for insomniac patient records.…”

Section: Schaltenbrand's System (Human Sleep Analysis)mentioning

confidence: 99%

See 1 more Smart Citation

Review of neural network applications in sleep research

Robert

Guilpin

Limoge

1998

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Electroencephalography (EEG)

Blinowska

Durka

2006

Wiley Encyclopedia of Biomedical Engineering

View full text Add to dashboard Cite

Electroencephalography is a domain concerning recording and interpretation of the electroencephalogram. Electroencephalogram (EEG) is a record of the electric signal generated by the cooperative action of brain cells, or more precisely, the time course of an extracellular field potentials generated by their synchronous action. Electroencephalogram derives from the Greek words enkephalo (brain) and graphein (to write). EEG can be measured by means of electrodes placed on the scalp or directly on the cortex. In the latter case, it is sometimes called electrocorticogram (ECoG). Electric fields measured intracortically were named Local Fields Potentials (LFP). EEG recorded in the absence of an external stimulus is called spontaneous EEG; EEG generated as a response to external or internal stimulus is called an event‐related potential (ERP. The amplitude of EEG of a normal subject in the awake state recorded with the scalp electrodes is 10–100 μV. In case of epilepsy, the EEG amplitudes may increase by almost an order of magnitude. In the cortex, amplitudes are in the range 500–1500 μV.

show abstract

Sleep Stage Scoring Using the Neural Network Model: Comparison Between Visual and Automatic Analysis in Normal Subjects and Patients

Cited by 139 publications

References 0 publications

Computer-assisted sleep staging

Computer-assisted sleep staging

Review of neural network applications in sleep research

Electroencephalography (EEG)

Contact Info

Product

Resources

About