Four all-night polysomnogranis of a 39-year-old male patient with non-21 11 sleep-wake syndrome were recorded. W e analysed electroericephalogram, (EEG) with the power spectrum method and the wave pattern recognition analysis of Fujiinori. T h e EEG of the rest waking condition showed normal patterns. High-voltage diffuse alpha band waves werc observed in sleep stages 2, 3 and 4. The integrated area of the alpha band waves in the analysis epochs showed a strong positive correlation to the delta band components in the power spectrum of the same e p c h during sleep (correlation coefficient5 r = 0.762-0.81 5). Alpha band wave5 during sleep were clearly different from the alpha waves in the r a t waking condition, with re\pect to slower peak frequency and the frontal dominant voltage distribution.
Summary a) In the course of our polygraphic study of sleep, a new method of EEG analysis was developed. As the result of a simple modification of the ordinary Walter type frequency analyser. we succeeded in obtaining a compact and easily observable record showing the long time variation of the integrated values of each band respectively. b) The application of this method to the study of sleep EEG opened a new aspect. 1. The most remarkable feature of this analysis is the undulatory fluctuation of relatively long time in the low frequency component (long undulatory fluctuation), especially in 1–2 cps band. In general, one sleep cycle is composed of a few of these undulatory fluctuations repeatedly appearing and the state of slight fluctuation mainly corresponding to the paradoxical phase. This long undulatory fluctuation corresponds to successive sleep phases in certain rule. The gradual change appearing at the time of a shift from the moderate phase to the deep phase is characteristic. 2. Generally the crest of the highest of these undulatory fluctuations during successive sleep cycles is highest at the first cycle and tends to decrease toward morning. The increasing slope is steepest at the first cycle and gentler toward morning. 3. During the ascending stage of this undulatory fluctuation the gross body movements are very few and in the other periods are relatively many. Most of the abrupt decreases of the integrated values of the low frequency band are accompanid by gross body movement. This report is dedicated to Professor Haruo Akimoto, Director of the Department of Neuropsychiatry, Faculty of Medicine, University of Tokyo, in commemoration of his sixtieth birthday. This study was carried out with the earnest cooperation of C. Sasaki, B.A. and N. Nagamura, M.A. We are much indebted to Mr. K. Akagi of San'ei Instrument Ltd. for the technical support.
A new mathematical method was developed to analyze time series. Applications of this method to the delta component of all-night sleep electroencephalogram (EEG) revealed new variations with double-rapid eye movement (REM)-sleep interval. The proposed method entails repeated application of the least squares spectrum. First, the conventional least squares spectrum calculation is applied to the time series. Using the parameters of the peak components in the obtained spectrum, an intermediate time series is reconstructed. The residual time series is made by subtracting the intermediate time series from the previous time series. Next, the least squares calculation is again applied to the residual time series. These procedures are repeated until the component cannot be detected. We named this new method the Repeated Least Squares Spectrum for the Residual (RLSSR). The remarkably similar time series pattern to the original time series can be reconstructed using the obtained parameters of all components. The EEG was recorded during all-night sleep on five consecutive nights in five healthy adults, by a total of 25 recordings. The variations of the height of successive 1-min delta components in the EEG were analyzed. In addition, two autocorrelograms were made for two time series patterns. These were reconstructed from two specific sets of the components obtained by the proposed analysis method. These autocorrelograms demonstrated a long-span variation with near double-REM-sleep interval in not a few records. Based on these data, it appears that the delta component of sleep EEG does not always demonstrate a simple gradually decreasing trend.
This paper presents material additional to a previous report on the repeated least squares spectrum for residual method (RLSSR) which was developed for describing long-span variations of the deltacomponent in sleep electroencephalograms. The RLSSR principally consists of a period-domain calculation based on the application of the least squares spectrum method. The processing allows for unlimited data series length, presents spectrum components with discrete distributions, and provides a good reproduction of the original pattern with the obtained components. These characteristics are quite different from those of the Fourier analysis which is a frequency-domain procedure. The accuracy of the spectrum depends on the ratio of the period between target and full process span. The other numerical characteristics are further clarified using dummy time series patterns. This method is effective to analyze the time series as period-domain spectrum in the biological and also in the other broad fields of science.
Summary All night sleep was recorded polygraphically on 14 healthy adults, once for each person, and EEG was analyzed with bandpass filters. The integrated values during successive 10 second epochs were recorded consecutively at 1 mm intervals, separately for each frequency band. The variations of integrated values for long time span were clearly observed, The findings on 1–2 Hz component are reported exclusively in this report. (1) Four variation patterns were classified on the variations of integrated values of 1–2 Hz band component; long undulation, short undulation, irregular undulation and slight fluctuation. Short undulation was superimposed on long undulation. Long undulation had a gradually increasing slope, a plateau and steeply decreasing slope. Irregular undulation showed generally irregular fluctuations, without any definite variation patterns. During slight fluctuation the variations were small. Long undulation and irregular undulation corresponded to slow‐wave sleep and slight fluctuation corresponded to REM sleep. The mean duration of each long undulation and irregular undulation was 31.2 and 11.9 minutes, respectively. In long undulation, 62.8 % of the time was occupied by Stage 2 and the lesser ratio by Stages 3 and 4. In irregular undulation 89.1 % of the time corresponded to Stage 2. (2) One sleep cycle was composed of the three sleep states which were accompanied with variation patterns, long undulation, irregular undulation and slight fluctuation. The sequence of the appearance of the former two patterns in one sleep cycle was classified into four types. About half of the sleep cycles in all records exhibited Type 1; the pattern started with long undulation, after one or several long undulations followed by irregular undulation and proceeded to slight fluctuation. The average number of long undulation in one sleep cycle was 1.58. (3) On all records, the highest long undulations appeared in the first cycle of all night sleep, in both frontal and central areas. In the occipital area, some of the highest crests appeared in the second cycle. When the highest of long undulations appeared, the height tended to decrease prior to wakening. The crest line of long undulation in frontal and central areas crossed in the latter period of all night sleep, in five controls. The mean voltage of long undulation in frontal, central and occipital areas Was 52.4, 42.5 and 23.5 microvolts, respectively. The underlying physiological mechanism of delta waves in human sleep EEG and Possible brain structures essential for long undulation and irregular undulation were discussed from both the clinical and experimental aspects.
The purpose of this research is to elucidate the amplitude variations of alpha band component in human electroencephalographic records during the transition between wakefulness and stage 1 sleep. The records from 16 adult male subjects were mathematically analyzed in successive 3-s epochs using a unique method of calculation (RLSSR). Amplitude variations are described herein. (i) General amplitudes are high during wakefulness and low during stage 1 sleep. Irregular fluctuations in amplitude are superimposed on these two levels. (ii) A large, steep, characteristic decline occurs during wakefulness. Two EEG patterns at the bottom of the decline represent arousal and EEG in stage 1 sleep, and these are referred to as big-decline-w and big-decline-s. The transition between the two levels appears as a big-decline-s rather than a gradual decrease. (iii) Thirteen records showed a multiple big-decline-s pattern before stage 1 sleep. The period from the first to the last bigdecline-s is referred to as the "approach period" to sleep. Three subjects had no such approach and only one big-decline-s that appeared at the end of wakefulness. (iv) The bottom of the last big-decline-s of the three sites, occipital, central and frontal, appeared simultaneously in 6 subjects, 9 had a one-epoch gap, and 1 had two such gaps. The decline of the slope of the last big-decline-s showed high regression to an exponential curve. It is suggested that this characteristic pattern is the significant index of psychophysiological transition from wakefulness to sleep.
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