相位式呼吸门控放疗中门控窗口的选择方法研究

目的:提出一种相位式呼吸门控放疗中门控窗口的选择方法。方法:采用数字表法随机选取某院已完成基于实时位置管理(real position management,RPM)系统的相位式呼吸门控放疗的38例患者。针对每个患者的参考呼吸波形,分别计算所有可选的100(10×10)个窗口内的呼吸信号标准差,作为衡量该窗口内呼吸运动稳定性的指标。在100个可选窗口中,将与临床所选窗口占空比相同但呼吸信号标准差最小的窗口作为建议窗口。比较并分析38例患者的临床窗口和基于该方法选择的建议窗口之间的呼吸信号标准差差异,并进行配对样本t检验。结果:38例患者中,临床窗口的呼吸信号标准差为(0.114±0.050)cm,建议窗口的呼吸信号标准差为(0.108±0.049)cm,二者相比有统计学差异(P=0.009)。27例患者的建议窗口与临床窗口一致,其余11例患者的建议窗口与临床窗口相比呼吸信号标准差有所减小(窗口内呼吸运动稳定性提高),其中1例下降幅度超过30%,8例下降幅度在5%~30%,2例下降幅度在5%以内。结论:在相位式呼吸门控放疗中,基于窗口内的呼吸信号标准差选择门控窗口的方法可以保证和提高临床所选门控窗口的质量。

基于小波特征的配电网线路运行工况识别

配电网线路是用电负荷与变电环节之间的传输介质,及时了解配电网线路的实时运行工况对于保证配电网供电可靠性极其重要。综合考虑现有配电线路工况识别算法的实际应用情况,设计了配电网线路运行工况识别系统,通过线路监测点实时采集了三相电流信号,提出了利用小波分析工具提取信号中的信号标准差、互相关系数以及小波奇异熵相比值3个特征量,采用电磁暂态仿真软件PSCAD/EMTDC建立了配电线路各种常见工况下的特征基因库,并将提取的特征量与特征库的规则相匹配来实现线路工况的识别。应用实例表明,提出的基于小波特征的识别方法具有较好的可靠性和准确性。

Automatically detecting auditory P300 in several trials

A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.

Comparative Analysis of EEG Signals Based on Complexity Measure

The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.

Transverse flowmetry of carbon particles based on photoacoustic Doppler standard deviation using an auto-correlation method

In order to measure the flow velocity of carbon particle suspension perpendicular to the receiving axis of ultrasound transducer, the standard deviation of photoacoustic Doppler frequency spectrum is used to estimate the bandwidth broadening, and the spectrum standard deviation is calculated by an auto-correlation method. A 532 nm pulsed laser with the repetition rate of 20 Hz is used as a pumping source to generate photoacoustic signal. The photoacoustic signals are detected using a focused PZT ultrasound transducer with the central frequency of 10 MHz. The suspension of carbon particles is driven by a syringe pump. The complex photoacoustic signal is calculated by Hilbert transformation from time domain signal before auto-correlation. The standard deviation of the Doppler bandwidth broadening is calculated by averaging the auto-correlation results of several individual A scans. The feasibility of the proposed method is demonstrated by measuring the spectrum standard deviation of the transversal carbon particle flow from 5.0 mm/s to 8.4 mrn/s. The experimental results show that the auto-correlation result is approximately linearly distributed within the measuring range.