Realisation d'un systeme automatique permettant de coder le sommeil des nourrissons (2 A 12 MOIS)

Samson-Dollfus, D; Nogues, B; Geffroy, Didier; Bertoldi-Lefeuver, Isabelle; Dreano, E.

doi:10.1016/0013-4694(82)90133-x

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Data from the 43 other recordings were used to calculate the other results as a function of sleep stage and age. The recording methods have been described previously (1,2). In summary, a visual coding (4,5) of paradoxical sleep (PS), quiet sleep (QS: 2-4) and transitional sleep (TrS) was performed on a 16-channel polygraphic recording (Alvar Electronic).…”

Section: Methodsmentioning

confidence: 99%

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Delta, Theta, Alpha and Beta Power Spectrum of Sleep Electroencephalogram in Infants Aged Two to Eleven Months

Samson-Dollfus

Nogues

et al. 1983

Sleep

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Summary: Automatic sleep EEG analysis was performed on infants from 2 to 11 months of age. Partial power spectra of 1>, e, Ct, and 131 bands were studied as function of sleep stages, age, and time of the night. 131' Ct, and 1> power spectra are significantly lower in paradoxical sleep (PS) than in quiet sleep (QS) whatever the age; but theta is lower in PS than in QS only after 5 months of age. 1>, e, and Ct power increase with age in QS. Only 1> and e are greater in the first half of the night than in the second half. 131 power does not differ significantly in stages 2 and 3 of QS, during the course of the night or as a function of age. Thus 1>, e, Ct may be the best spectral parameters for the maturation of quiet sleep EEGs during the first year of life. Key Words: Automatic analysis-Sleep EEG-Infants.We have previously developed and reported a system of automatic computerized analysis of sleep in infants (1,2), which is valid for full-term infants aged 2-11 months. U sing data from these subjects we studied the development of different EEG frequency bands as a function of sleep stage and course of the night during the first year of life. SUBJECTS AND METHODSTwenty infants younger than 5 months of age and 23 infants aged 5 -11 months were recorded during sleep (Table O. The younger group was recorded during the day (morning sleep), the older group during the first half of the night. Eighteen other infants aged 3-11 months had all-night recordings. These 61 full-term infants were either control subjects, with no serious neurological or medical pathology, receiving no medication, or siblings of infants who had died of Sudden Infant Death Syndrome (SIDS). Sterman et aI. (3) have found only a few minor differences in SIDS siblings as compared with normal subjects at 4 and 8 weeks of age. All of the infants in our study were older than 8 weeks of age, and no differences were found by Sterman et al. in these older babies. Data from the 18 children who had full-night recordings were used for a com-

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Section: Methodsmentioning

confidence: 99%

“…We have previously developed and reported a system of automatic computerized analysis of sleep in infants (1,2), which is valid for full-term infants aged 2-11 months. U sing data from these subjects we studied the development of different EEG frequency bands as a function of sleep stage and course of the night during the first year of life.…”

mentioning

confidence: 99%

Delta, Theta, Alpha and Beta Power Spectrum of Sleep Electroencephalogram in Infants Aged Two to Eleven Months

Samson-Dollfus

Nogues

et al. 1983

Sleep

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“…1980;Samson-Dollfus et al, 1980, 1982 and to the discussion of the corresponding statistical methodology [Agnew, 1973;Hoffmann et al, 1979;Etevenon and Giannella, 1980].…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Multivariate Statistical Analysis of Sleep Stages

Molinari¹,

Dumermuth²,

Lange³

1984

Neuropsychobiology

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All-night sleep of 5 healthy male subjects was scored on the basis of EEG (paper records), EMG and EOG into the stages of wakefulness, REM sleep and slow sleep stages 1–4 according to Rechtschaffen and Kales. Spectral analysis for 8 EEG channels was then performed and spectral parameters (total power and coherence for certain frequency bands) extracted. Stepwise linear discriminant analysis was applied to these spectral parameters to see how well the polygraphically defined sleep stages were recognized. While the results of each intrasubject analysis were satisfactory (error rates between 10 and 15 %), a more detailed analysis revealed that most errors occur between nearby slow sleep stages or between S1 and REM or wakefulness. This is related to the fact that Rechtschaffen and Kales classify into discrete stages an essentially continuous process and this is, to some extent, arbitrary. This interpretation is supported by examples. Cross-classification of each subject on the basis of the remaining 4 increases the error rates drastically. A simple way of avoiding this effect, by standardizing the individual data, is shown to work nicely in this case.

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Mise au point chez le nourrisson d'un critere de detection automatique des fuseaux de sommeil

Delapierre

Dreano

Samson-Dollfus

et al. 1986

Revue d&'apos;Electroencéphalographie et de Neurophysiologi

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Realisation d'un systeme automatique permettant de coder le sommeil des nourrissons (2 A 12 MOIS)

Cited by 4 publications

References 11 publications

Delta, Theta, Alpha and Beta Power Spectrum of Sleep Electroencephalogram in Infants Aged Two to Eleven Months

Delta, Theta, Alpha and Beta Power Spectrum of Sleep Electroencephalogram in Infants Aged Two to Eleven Months

EEG-Based Multivariate Statistical Analysis of Sleep Stages

Mise au point chez le nourrisson d'un critere de detection automatique des fuseaux de sommeil

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