Agreement in visual scoring of sleep stages among laboratories in Japan

Kim, Young-do; Kurachi, Masayoshi; Horita, Motoshi; Matsuura, Kiyotaka; Kamikawa, Yasuko

doi:10.1111/j.1365-2869.1992.tb00011.x

Cited by 13 publications

(12 citation statements)

References 6 publications

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“…Staging criteria defined in Rechtschaffen and Kales (1968) leave a significant margin for subjective interpretation; therefore, hypnograms constructed for the same recording, even by experts from the same laboratory, can be slightly different (Kim et al 1992;Monroe 1967). This effect is illustrated in Fig.…”

Section: Resultsmentioning

confidence: 98%

Fully Parametric Sleep Staging Compatible with the Classical Criteria

et al. 2009

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We present an open system for sleep staging, based explicitly on the criteria used in visual EEG analysis. Slow waves, theta and alpha waves, sleep spindles and K-complexes are parameterized in terms of time duration, amplitude, and frequency of each waveform by means of the matching pursuit algorithm. It allows the detection of these structures using mostly the criteria from visual EEG analysis. For example, within this framework for the first time we compute directly the relative duration of slow waves, which is a basic parameter in recognition of deep sleep stages. Performance of the system is evaluated on 20 polysomnographic recordings, scored by experienced encephalographers. Seven recordings were scored by more than one expert. Proposed system gives concordance with visual staging close to the inter-expert concordance. The algorithm is implemented in a user-friendly software system for display and analysis of polysomnographic recordings, freely available with complete source code from http://signalml.org/svarog.html .

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Section: Resultsmentioning

confidence: 98%

Fully Parametric Sleep Staging Compatible with the Classical Criteria

et al. 2009

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“…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. Visual scoring of two healthy subjects in ten laboratories in Japan showed 67%-75.3% agreement [7].…”

Section: Introductionmentioning

confidence: 99%

“…Studies have shown interscorer agreement ranging from 67% to 91% [4]- [7] depending on different scoring epoch lengths and number of readers. Visual scoring of two healthy subjects in ten laboratories in Japan showed 67%-75.3% agreement [7]. Most data on interscorer agreement are based on the study of normal subjects.…”

Section: Introductionmentioning

confidence: 99%

Computer-assisted sleep staging

Agarwal

Gotman

2001

IEEE Trans. Biomed. Eng.

194

101

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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.

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“…Each epoch is described by the power in these bands. The classical band definitions in EEG are used -delta(0-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12), sigma(12-15 Hz), beta1(15-24 Hz), beta2(24-36 Hz). The power spectral density (PSD) for each QSS is calculated using the Fast Fourier transform (FFT) with mean removed.…”

Section: B Eeg Compression Methodsmentioning

confidence: 99%

Compression of long-term EEG using power spectral density

Madan

Agarwal

Swamy

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper, we propose to use the features based on power spectral density as a descriptor of the EEG in the compression of the long-term intensive care unit EEG to obtain the temporal evolution of the recurrent patterns. Sleep EEG is used as a baseline since the sleep stages can be mapped to recurrent patterns in the background EEG. Our results indicate that the spectral features provide a better classification of the sleep EEG and assist in a better formation of homogenous clusters compared to the results obtained with the previously used features. The average overall agreement compared against manual scoring of seven sleep EEG records is 68.5%. It is an improvement compared to 62.7% obtained with the previously used features. Although our results for computer classification use only the EEG information from one frontal and one occipital channel, they are similar to the manual classification of sleep EEG, which is based on additional information.

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Agreement in visual scoring of sleep stages among laboratories in Japan

Cited by 13 publications

References 6 publications

Fully Parametric Sleep Staging Compatible with the Classical Criteria

Fully Parametric Sleep Staging Compatible with the Classical Criteria

Computer-assisted sleep staging

Compression of long-term EEG using power spectral density

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