All-night spectral analysis of the sleep EEG in untreated depressives and normal controls

Borbély, Alexander A.; Tobler, Irene; Loepfe, M; Kupfer, David J.; Ulrich, Richard F.; Grochocinski, Victoria J.; Doman, Jack; Matthews, G. A.

doi:10.1016/0165-1781(84)90135-5

Cited by 158 publications

(75 citation statements)

References 6 publications

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“…Similar findings have been reported in [19] and in some other researches, decrease of delta activity in first cycle of sleep or in NREM sleep of depressed people also was expressed [6,20]. The results of studying mean amplitude of power spectrum and power of signals in time domain in alpha band revealed that activity of this band has been lower in depressed patients but these differences only approached significance (P=0.05) in first period of REM in channel C3.…”

Section: Discussionsupporting

confidence: 75%

“…Borbély et al have reported that a power spectral density of sleep EEG in 0.25 to 2.5 Hz in depressed patients is lower than normal. They also shown that the power density in the entire frequency range (0.25 to 25 Hz) exhibited a decreasing trend over the first three NREM/REM sleep cycles for both groups, but in each cycle depressed people had lower values than controls [6], although this result was breached in later years by Mendelson et al [7]. Tekell and colleagues in their work demonstrated increase of high frequencies activity in these patients [15].…”

Section: Introductionmentioning

confidence: 93%

“…Depressed patients complain of insomnia (i.e., prolonged sleep latency, frequent awakenings during sleep, early morning awakening), disturbing dreams and nightmares and bad quality of sleep. These symptoms can accompanied with difficulty in maintaining sleep, decrease of total sleep time and quality, abnormalities of REM sleep, changes in the structure of NREM sleep and variables derived from quantitative sleep electroencephalogram (EEG) analysis [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Diagnose of Depression in Subjects with Sleep Disorders using Analysis of REM Sleep Electroencephalogram signals

Marzieh¹

2014

J Sleep Disord Ther

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By considering the prevalence of depression in subjects with sleep disorders, our purpose is diagnosis of depression in these subjects. Shortened REM latency (i.e, the interval between sleep onset and the occurrence of the first REM period), increased REM sleep duration and increased REM density (i.e., the frequency of rapid eye movements per REM period) have been considered as biological markers of depression, so to diagnose of depression in subjects with sleep disorders we used analysis of EEG signal in REM sleep. The features of frequency and time domains in four channels (C3-C4-O1-O2) and four bands (delta, alpha, beta ,gamma) of REM sleep EEG were compared in 10 depressed (37.5 ± 8.34 years; mean ± standard deviation) and 14 non-depressed (36.6 ± 11.1 years; mean ± standard deviation) subjects with sleep disorders. According to results, most of the significant differences (P<0.05) have related to C3 and O1, in first stage and first period of REM sleep. Our results also show significant increase of beta activity in depressed subjects. Mahalanobis distance has classified 91.7% of the depressed and non-depressed subjects correctly.

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Section: Discussionsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Diagnose of Depression in Subjects with Sleep Disorders using Analysis of REM Sleep Electroencephalogram signals

Marzieh¹

2014

J Sleep Disord Ther

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“…In case of the spectrum analysis, we do not expect to easily achieve further significant improvements, because much effort in this respect already has been made in many previous studies [25,26]. Other studies have shown that the delta/beta ratio provides the most information about the alterations of the sleep structure [27].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

et al. 2008

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In healthy subjects, sleep has a typical structure of three to five cyclic transitions between different sleep states. In major depression, this regular pattern is often destroyed but can be reestablished during successful treatment. The differences between healthy and abnormal sleep are generally assessed in a time-consuming process, which consists of determining the nightly variations of the sleep states (the hypnogram) based on visual inspection of the electroencephalogram (EEG), electrooculogram, and electromyogram. In this study, three different methods of sleep EEG analysis (spectrum, outlier, and recurrence analysis) have been examined with regard to their ability to extract information about treatment effects in patients with major depression. Our data suggest that improved sleep patterns during treatment with antidepressant medication can be identified with an appropriate analysis of the EEG. By comparing different methods, we have found that many treatment effects identified by spectrum analysis can be reproduced by the much simpler technique of outlier analysis. Finally, the cyclic structure of sleep and its modification by antidepressant treatment is best illustrated by a non-linear approach, the so-called recurrence method.

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“…Typically, the quality of sleep is estimated from the calculation of delta power, the power of EEG/LFP events in a frequency <4 Hz [8,9]. Although SWs are the main elements that contribute to the EEG delta power (0.2-4 Hz), they are not synonymous [10] and the number and other characteristics of the SWs such as amplitude and duration cannot be directly estimated with simple filtering.…”

Section: Introductionmentioning

confidence: 99%

Supervised semi-automatic detection of slow waves in non-anaesthetized mice with the use of neural network approach

Bukhtiyarova¹,

Soltani²,

Chauvette³

et al. 2016

Transl Brain Rhythmicity

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Slow waves (SWs) are EEG or local field potential (LFP) events that are present preferentially during slow-wave sleep and reflect periods of synchronized hyperpolarization followed by depolarization of many cortical neurons. We developed a new algorithm of supervised semi-automatic SW detection based on pattern recognition of the original signal with artificial neural network. The method enabled fast analysis of long-lasting recordings in non-anaesthetized freely behaving mice. It allowed finding tens of thousands of SW in 24-hour period of recording with their density in the order of 1.3 SW per second during slow-wave sleep and 0.03 SW per second during waking state. Occasional SWs were also found in REM sleep. The proposed algorithm can be used for off-line and on-line detection of SW.

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All-night spectral analysis of the sleep EEG in untreated depressives and normal controls

Cited by 158 publications

References 6 publications

Diagnose of Depression in Subjects with Sleep Disorders using Analysis of REM Sleep Electroencephalogram signals

Diagnose of Depression in Subjects with Sleep Disorders using Analysis of REM Sleep Electroencephalogram signals

Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

Supervised semi-automatic detection of slow waves in non-anaesthetized mice with the use of neural network approach

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