The effect of small differences in electrode position on EOG signals: application to vigilance studies

260

211

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 × 7™) 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™). 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 × 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 × 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 × 7 and demonstrated its applicability in clinical routine and sleep studies.

Section: Polysomnographic Recordingsmentioning

confidence: 99%

An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

Anderer

Gruber²,

Parapatics³

et al. 2005

260

211

“…The bedtime of the subjects had to be between 22.00 and 24.00 h. Six EEG channels (Fp1-A2, Fp2-A1, C3-A2, C4-A1, O1-A2, O2-A1), two EOG channels (P8-A1, P18-A1 [4]) and two EMG channels (mental, linked tibialis anterior) were digitally recorded with either 200-or 256-Hz sampling rates. The first night was for adaptation.…”

Section: Subjects and Recordingsmentioning

confidence: 99%

A Study on Gender and Age Differences in Sleep Spindles

Huupponen

Himanen²,

Värri³

et al. 2002

In the present work, gender differences in sleep spindle topography were examined in 40 subjects. Their median age was 32 years (range 22–49 years). Spindles were detected from 3,306,060 s of visually scored stage 2 sleep EEG by a previously validated automatic fuzzy detector at 1-second intervals. A total of 271,168 spindles were found from the six EEG channels analyzed. Females showed a significantly higher percentage of spindles in the left frontal channel than males (Fp1-A2; p = 0.026). To confirm that this difference was gender and not age related, the subjects were divided into two age groups. No significant differences in spindle activity of the frontal channels were found between the groups. However, the interindividual spindle variability seemed to be at least as large as that stemming from gender.

“…Since the electrical field generated by the eyes decreases exponentially with the distance from the ocular dipole (Elbert et al [43]), the best EOG to EEG ratio in the EOG channels is achieved when EOG electrodes are placed as close as possible to the eyes. Even though this demand is satisfied if electrode position 8 and 18 according to Häkkinen et al [18] are used, two unipolar EOG leads are not sufficient to measure all the ocular variance. At least two orthogonal bipolar recordings (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Examples of 20-second polygraphic sleep recordings contaminated by various artifacts in different sleep stages are shown for a 41-year-old healthy female subject in figures 1-5: channels 1-3: electrodes at the left hemisphere referenced to right mastoid (Fp1, C3, O1-A2); channels 4-6: electrodes at the right hemisphere referenced to left mastoid (Fp2, C4, O2-A1); channel 7: left to right mastoids (A1-A2) for transformation into a symmetric reference; channels 8, 9: electrodes above left and below right outer canthus referenced to left mastoid (Pos8, Pos18-A1, according to Häkkinen et al [18]; channel 10: submental EMG. EEG and EOG channels were recorded with a time constant of 1.56 s and a high-frequency filter of 75 Hz.…”

Section: Types Of Artifactsmentioning

confidence: 99%

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

Anderer¹,

Roberts²,

Schlögl³

et al. 1999

139

Quantitative analysis of sleep EEG data can provide valuable additional information in sleep research. However, analysis of data contaminated by artifacts can lead to spurious results. Thus, the first step in realizing an automatic sleep analysis system is the implementation of a reliable and valid artifact processing strategy. This strategy should include: (1) high-quality recording techniques in order to minimize the occurrence of avoidable artifacts (e.g. technical artifacts); (2) artifact minimization procedures in order to minimize the loss of data by estimating the contribution of different artifacts in the EEG recordings, thus allowing the calculation of the ‘corrected’ EEG (e.g. ocular and ECG interference), and finally (3) artifact identification procedures in order to define epochs contaminated by remaining artifacts (e.g. movement and muscle artifacts). Therefore, after a short description of the types of artifacts in the sleep EEG and some typical examples obtained in different sleep stages, artifact minimization and identification procedures will be reviewed.