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This study is done to clarify the clinical meaning of "Automated Fluctuation Analysis" of high frequency EEG in man especially focused on the fine alteration of consciousness level of the subjects. Twenty normal volunteers were utilized for this study. They were divided into two groups, the subjects who felt sleepiness (Group S; N = 8) and the subjects who denied it (Group W; N = 12), during the EEG recording. "Automated Fluctuation Analysis" of high frequency EEG had been presented in our previous paper. In short, it is made of three steps, amplification of EEG signal, A/D conversion and Fast Fourier Transform by signal processor and extraction of Lorentzian parameters. Power spectral density (PSD) was displayed on log-log graph. Then the third step is performed by the best curve fitting program to the following equation, S (f) = S1/[1 + (f/fc1)2] + S2/ [1 + (f/fc2)2], where S(f) is power spectral density at any frequency f, S1 and S2 are plateau level of initial and second Lorentz, respectively and fc1 and fc2 are the corner or half power frequency of initial and second Lorentz, respectively. The algorithm of this program to extract these parameters were mathematically based on Brown & Dennis. As results, 1. PSD of human high frequency EEG was composed of double Lorentzians and vanished into white level within 1kHz. 2. A topographical display of S1 value revealed hyperfrontal in group W, which is in accordance with the cerebral blood flow study by Ingvar.(ABSTRACT TRUNCATED AT 250 WORDS)
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PMID:[Electrophysiological evaluation of human consciousness level by "Automated Fluctuation Analysis" of human high frequency EEG]. 129 28

Nicotinic acetylcholine-receptor ion channels (AChR channels) were studied in bullfrog sympathetic ganglion cells cultured for 1 day to 3 weeks, using a patch clamp technique. Microsuperfusion of ACh (2-10 microM) to the ganglion cell under the whole cell clamp produced an inward current at membrane potentials negative to -60 mV, which had a fast onset and decay. This rapid ACh-induced current was accompanied by a large current fluctuation, decreased and increased in amplitude by membrane depolarization and hyperpolarization, respectively, and blocked by d-tubocurarine. Thus, this current must be induced by the nicotinic action of ACh, but not by a muscarinic effect to activate a slow cation-selective current. At depolarized levels more than -50 mV, ACh induced an additional inward current which was slow in time course, accompanied by no or decreased current fluctuation and increased in amplitude by membrane depolarization. Accordingly, this slow ACh-induced current could result from the suppression of a voltage-dependent K+ current (M-current: Brown and Adams 1980) by the muscarinic action of ACh. Fluctuation analysis of the rapid ACh-induced current at potentials negative to -50 mV revealed the elementary conductance of 14 pS and a power spectral density distribution of the double Lorentzian function which yielded the time constants of 5.4 and 62.5 ms at -60 to -80 mV. The variance of either component was independent of the mean current.
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PMID:Patch clamp experiments on nicotinic acetylcholine receptor-ion channels in bullfrog sympathetic ganglion cells. 275 69

Since Berger's discovery of the electroencephalogram (EEG), its analysis has been generally restricted to the visual range (upmost 100Hz) and has ignored higher frequency components. One reason should be that there are no reliable methods to distinguish the brain potentials from muscle activity. We have introduced fluctuation analysis, which is popular method especially in the field of basic physiology to clinical electrophysiology. In our previous study, it was declared that power spectral density (PSD) of human high frequency EEG was composed of double Lorentzians and vanished into white level within 1kHz. Then the purpose of this study is to elucidate the "Automated Fluctuation Analysis," which enables us to evaluate these higher frequency components and its physiological meaning especially focused on conscious level from wakefulness to sleep stage 1. Seventy-four scalp recording EEGs in twenty normal subjects were studied. In short, "Automated Fluctuation Analysis" is made of three steps: amplification of EEG signal, A/D conversion and Fast Fourier Transform by signal processor and extraction of Lorentzian parameters. PSD of high frequency EEG was displayed on log-log graph and the algorithm fit to the following Lorentzian formula were mathematically based on Brown & Dennis. S(f) = S1/[1+(f/fc1)2]+S2/[1+(f/fc2)2], where S(f) is PSD (mu V2/Hz) at each frequency (f;Hz), S1 and S2 are the plateau level or zero-frequency power of the initial and second Lorentz, and fc1 and fc2 are the corner or half-power frequency of the initial and second Lorentz, respectively. As results, during wakefulness the PSD of high frequency EEG activity was composed of double Lorentzian fluctuations and the power distribution of S1 value in topographical display was frontal dominant. This pattern of S1 value disappeared and S2 value became lower during sleepiness and the second Lorentz disappeared during sleep.
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PMID:Evaluation of human consciousness level by means of "Automated Fluctuation Analysis" of high frequency electroencephalogram fitted by double Lorentzians. 811 79

Upright sitting is one of the first motor skills an infant learns, and thus sitting postural control provides an early window into the infant's motor development. Early identification of infants with motor developmental delay, such as infants with cerebral palsy, allows for early therapeutic intervention by physical therapists. Early intervention is thought to produce better outcomes, due to greater neural plasticity in younger infants. Postural sway, as measured by a force plate, can be used to objectively and quantitatively characterize infant motor control during sitting. Pathology, such as cerebral palsy, may alter the fractal properties of motor function. Often physiologic time series data, including infant sitting postural sway data, is mathematically non-stationary. Detrended Fluctuation Analysis (DFA) is useful to characterize the fractal nature of time series data because it is does not assume stationarity of the data. In this study we found that suitable selection of the order of the detrending function improves the performance of the DFA algorithm, with a higher order polynomial detrending better able to distinguish infant sitting posture time series data from Brown noise (random walk), and first order detrending better able to distinguish infants with motor delay (cerebral palsy) from infants with typical development.
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PMID:Nonlinear detrended fluctuation analysis of sitting center-of-pressure data as an early measure of motor development pathology in infants. 1978 Nov 35