In this paper,the authors expounded the concept and theoretical basis of the equifrequency geoid,discussed the relation between the equi-frequency scold and the classic geoid,proposed a new method──gravitation or gravity frequency shift method──for determining geopotential differences and heisht differences,and probed the way to solve the geodetic boundary value problem by using the method of frequency--shift measurement.

The gravity frequency (fg) and complexity of KC along with the analysis of Heart Rate Variability(HRV)are used for changes of EEG signals. Furthermore, the fatigue model's effects on EEG were studied in four aspects: 1. SD model's effectiveness on sleep stages;

In addition, considering the origin, the mechanism and the framework of the pulse signals, peak value, peak frequency, center of gravity (cg), gravity frequency of power spectrum, AR model parameter, the value of SER and Renyi entropy were extracted.

Only two sub-healthy personsare misjudged. The recognition accuracy of 80% was attained by using eg and gravity frequency of power spectrum as characteristics.

Method Cortical EEG and hippocampal potential were collected by implanted electrodes in freely moving rats. Algorithmic complexity (Kc), approximate entropy (ApEn), power spectral density (PSD) and gravity frequency of PSD of the potential waves were calculated.

In this paper,the authors expounded the concept and theoretical basis of the equifrequency geoid,discussed the relation between the equi-frequency scold and the classic geoid,proposed a new method──gravitation or gravity frequency shift method──for determining geopotential differences and heisht differences,and probed the way to solve the geodetic boundary value problem by using the method of frequency--shift measurement.

Objective To study the complexity and the power spectrum of cortical EEG and hippocampal potential in rats under waking and sleep states. Method Cortical EEG and hippocampal potential were collected by implanted electrodes in freely moving rats. Algorithmic complexity (Kc), approximate entropy (ApEn), power spectral density (PSD) and gravity frequency of PSD of the potential waves were calculated. Result The complexity of hippocampal potential was higher than that of cortical EEG under every state. The...

Objective To study the complexity and the power spectrum of cortical EEG and hippocampal potential in rats under waking and sleep states. Method Cortical EEG and hippocampal potential were collected by implanted electrodes in freely moving rats. Algorithmic complexity (Kc), approximate entropy (ApEn), power spectral density (PSD) and gravity frequency of PSD of the potential waves were calculated. Result The complexity of hippocampal potential was higher than that of cortical EEG under every state. The complexity of cortical EEG was lowest under the state of non rapid eye movement(NREM) sleep. The complexity of hippocampal potential was highest under waking state. The total power of both potentials in 0.5～30 Hz frequency band showed their highest values under NREM state. Conclusion The values of Kc and ApEn are closely related to the distributions of PSD. When there are evident peaks in PSD, the complexities of signals will decrease. The complexities may be used to distinguish the difference between cortical EEG and hippocampal potential, or large differences between the same kind of potentials under different behavioral states

AIM To compare the new methods which can be used fo r soring the stages of rat sleep. METHODS Cortical EEG and hippocampal potential(HP) were collected by implanted electrodes in freely moving rats. Gravity frequency (f g) and the complexities(Kc,C 1), were calculated. According to the characteristics of each stage and by studying the histogram of f g, Kc and C 1, the stages of rat sleep were distinguished. RESULTS The methods of f g, Kc and C 1 can distinguish the Waking, nonrapid eye movement(NREM)...

AIM To compare the new methods which can be used fo r soring the stages of rat sleep. METHODS Cortical EEG and hippocampal potential(HP) were collected by implanted electrodes in freely moving rats. Gravity frequency (f g) and the complexities(Kc,C 1), were calculated. According to the characteristics of each stage and by studying the histogram of f g, Kc and C 1, the stages of rat sleep were distinguished. RESULTS The methods of f g, Kc and C 1 can distinguish the Waking, nonrapid eye movement(NREM) and REM sleep well. Comparing the results of visual and our analysis, the rate of accordance is above 85%, and the analysis based on the complexity measure is more believable than that based on the f g. CONCLUSION The gravity frequency and the complexities of Kc,C 1 can be used to distinguish the different stages of rat sleep.