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  • 1
    ISSN: 1432-2072
    Keywords: Key words Microstructure ; Sleep EEG ; Spectral power analysis ; Paroxetine ; Healthy subjects
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Paroxetine is a selective and potent serotonin reuptake inhibitor and its efficacy for the treatment of depression has been proven. Under acute and subchronical treatment regimens, disturbances of the regular sleep pattern are a reported side effect of the drug. The present study was therefore performed to investigate the impact of subchronic treatment with the selective serotonin reuptake inhibitor paroxetine on the microstructure of the sleep EEG. The study especially addressed the question of subchronic effects of paroxetine medication (30 mg/day) in eight healthy male volunteers in a double blind, placebo-controlled crossover design. Conventional sleep EEG parameters and a spectral power analysis for different sleep stages after 4 weeks of treatment were computed. Additionally, the correlation of certain EEG rhythms across the night was calculated in order to detect subtle dynamical EEG alterations, not necessarily obvious when regarding conventional EEG analysis. Although we could not detect any alterations of the spectral power values in certain frequency bands either during NREM nor during REM sleep following subchronic paroxetine medication, the dynamical EEG attributes across the night revealed a significant enhancement of the correlation between certain EEG rhythms mainly during NREM sleep.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    European archives of psychiatry and clinical neuroscience 245 (1995), S. 27-35 
    ISSN: 1433-8491
    Keywords: Sleep EEG ; Lyapunov exponent ; Deterministic chaos ; Nonlinear dynamics ; Depression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Conventional sleep analysis according to Rechtschaffen and Kales (1968) has provided meaningful contributions to the understanding of disturbed sleep architecture in depression. However, there is no characteristic alteration of the sleep cycle, which could serve as a highly specific feature for depressive illness. Therefore, we started to investigate nonlinear properties of sleep electroencephalographic (EEG) data in order to elucidate functional alterations other than those obtained from classical sleep analysis. The application of methods from nonlinear dynamical system theory to EEG data has led to the assumption that the EEG can be treated as a deterministic chaotic process. Chaotic systems are characterized by a so-called sensitive dependence on initial conditions. This property can be quantified by calculating the system's Lyapunov exponents, which measure the exponential separation of nearby initial states in phase space. For 15 depressive inpatients (major depressive episodes according to DSM-III-R criteria) and 13 healthy controls, matched in gender, age, and education, we computed the principal Lyapunov exponents L1 of EEG segments corresponding to sleep stages I, II, III, IV, and rapid eye movement (REM), according to Rechtschaffen and Kales, for the lead positions CZ and PZ. We found statistically significant decreased values of L1 during sleep stage IV in depressives compared with a healthy control group.
    Type of Medium: Electronic Resource
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