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Objective To analyze the energy of rolling manipulation in different frequency bands and find the features of rolling manipulation dynamics. MethodThe force signals of rolling manipulation of six experts and six beginners were measured and divided into different frequency bands by wavelet transform to calculate the energy. Through statistical analysis, 18 characteristic quantities of horizontal force or vertical force were created and the overall evaluation coefficient R was proposed. ResultsAbout 70% of experts’ rolling manipulation energy was found in 0~0.406 25 Hz and about 20% energy in 1.625~3.25 Hz. The overall evaluation coefficient R of 6 experts was over 0.70, while R of beginners was below 0.70, which showed the difference was significant. ConclusionsThe energy distribution of rolling manipulation reflects the characteristics of softness and periodicity. If the rolling manipulation is in accordance with the manipulative requirement and the overall evaluation coefficients R is over 0.70, it could be said that the operator masters the rolling manipulation well.
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Objective To detect the change of discharge phase of guinea pig hippocampal CA1 pyramidal cells during visual discriminative task with an effective and convenient program we designed. Methods Five guinea pigs were performed by extracellular single unit recording in vivo when they were performing visual discriminative task. Discharge signals of individual pyramidal cells were extracted from different frequency signals by wavelet transform (WT), which made it feasible to calculate discharge phase of pyramidal cells in terms of time correlation between discharge and ? rhythm. Results The discharge phase of CA1 pyramidal cells in the 1 to 5s interval before visual discriminative task (172??1.8?) was obviously earlier than that in the 6 to 10s interval after visual discriminative task (189??3.7?) ( P0.01). Conclusion The program we designed is capable of detecting discharge phase of pyramidal cells. Regular shift of discharge phase of hippocampal CA1 pyramidal cells emerges before and after performing visual discriminative task.
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Wavelet transforms are used in detecting ECG characteristic points recognition.The relationships between ECG characteristic points and the modulus maximum pairs of their wavelet transfoms are expounded.The application perspective of wavelet transforms to ECG characteristic points recognition is predicted through the analysis of the results.