Adaptive denoising and multiscale detection of the V wave in brainstem auditory evoked potentials

This paper describes a wavelet-transform-based system for the V wave identification in brainstem auditory evoked potentials (BAEP). The system combines signal denoising and rule-based localization modules. The signal denoising module has the potential of effective noise reduction after signal averaging. It analyses adaptively the evolution of the wavelet transform maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. A non-linear filtering process implemented with a neural network extracts out the noise-induced maxima. The filtered wavelet details are subsequently analysed by the rule-based localization module for the automatic identification of the V wave. In the first phase, it implements a set of statistical observations as well as heuristic criteria used by human experts in order to classify the IV-V complex. At the second phase, using a multiscale focusing algorithm, the IV and V waves are positioned on the BAEP signal. Our experiments revealed that the system provides accurate results even for signals exhibiting unclear IV-V complexes.     

基于数据降噪的冷轧板形调控功效系数获取

 针对轧制过程实际数据噪声大、难以获取准确板形调控功效系数的问题,提出了一种融合集成经验模态分解(EEMD)和小波变换(WT)的数据降噪方法。将含有噪声的实际生产数据经过EEMD分解后,利用小波变换方法对噪声主导的本征模态分量(IMF)进行降噪处理,处理后的噪声分量与其余分量重构得到降噪后数据,并结合结构方程模型(SEM)计算得到板形功效系数。利用1 450 mm五机架冷连轧生产线实际数据进行试验,结果表明,EEMD-WT-SEM方法可以有效降低数据噪声,有效提升板形调控功效系数的准确性。     

Medication side effects in anxious patients: negative placebo responses?

Heart sounds produce an incessant noise during lung sounds recordings. This noise severely contaminates the breath sounds signal and interferes in the analysis of lung sounds. In this paper, the use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is presented. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. In addition, the use of hard thresholding in the wavelet transform domain results in a separation of the nonstationary part of the input signal (heart sounds) from the stationary one (lung sounds). Thus, the location of the heart sound noise (1st and 2nd heart sound peaks) is automatically detected, without requiring any noise reference signal. Experimental results have shown that the implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of the superimposed heart sound noise, producing an almost noise-free output signal. Due to its simplicity and its fast implementation the method can easily be used in clinical medicine.     

小波遗传神经网络-阳极溶出伏安法同时测定钢中铜和铅

研究了二硫代二安替吡啉甲烷作修饰剂的修饰玻碳电极,并以此为工作电极,建立了测定铜和铅的阳极溶出伏安法。应用小波变换去噪、数据压缩和背景扣除功能对峰电流信号进行处理,用遗传算法自适应概率搜索神经网络的最优网络结构和参数,解决神经网络过拟合问题,将小波神经网络(WT-BP-ANN)、小波遗传神经网络(WT-GA-BP-ANN)的分析结果进行比较,Cu2+,Pb2+测定结果的RSD分别为3.49%,2.33%;3.77%,1.89%,表明小波遗传神经网络优于小波神经网络。     

基于小波分解的尾矿坝浸润线预测方法研究

为了准确预测尾矿坝浸润线的位置变化,结合浸润线埋深非稳定、非线性的时间序列以及动态变化的特点,利用小波分解与重构,提出基于小波分解的时间序列指数平滑法和BP神经网络法,采用时间序列的指数平滑法和BP神经网络方法分别对多个细节信号序列和逼近信号序列进行拟合预测,并对其拟合结果进行叠加,实现对尾矿坝浸润线的预测。将预测结果与实际监测数据进行对比,结果表明小波分解预测方法的预测结果与传统单一的指数平滑法和神经网络法预测结果相比,在预测精确度和拟合度方面：小波分解>指数平滑>神经网络。     

大型连轧机传动齿轮在线故障诊断方法研究

在大型连轧机组中，主传动系统的减速机、齿轮在受着巨大的负载，很容易出现各种故障，采用神经网络自适应滤波方法和小波分解方法在线分析传动齿轮的振动信号，有效地分解出信号中由故障所引起的成分，地诊断出连轧机传动齿轮的各种故障原因，为大型连轧机组齿轮传动系统的故障在线监测提供了新的有效的工具。     

Amantadine susceptibility in influenza A virus isolates: determination methods and lack of resistance in a Canadian sample, 1991-94

OBJECTIVES: To learn which fetal heart rate (FHR) parameters change with gestational age and to demonstrate the relation with fetal rest-activity states. STUDY DESIGN: FHR and fetal movements were recorded in 12 uncomplicated pregnancies from 26 weeks gestational age onwards. Seventy-two FHR recordings of 60 min duration were analysed by a computer (Sonicaid System 8000). Statistical analysis of complete 60 min recordings and selective periods of rest and activity comprised Spearman's rank correlation test, regression analysis and Wilcoxon's signed-rank test. RESULTS: The time needed to meet the system's criteria of normality decreased with gestational age. The incidence of accelerations (ACC), overall FHR variation (VAR) and variation during 'episodes of high variation' (VEHV) increased with gestational age in the total population, but statistical significance of these relations could only be demonstrated in a minority of individual fetuses. Most FHR parameters differed significantly for periods of fetal rest and activity. No FHR parameters showed a relation with gestational age during periods of rest. CONCLUSIONS: The increase of ACC, VAR and VEHV with gestational age is primarily due to an increase during fetal activity. The considerable variation within and between fetuses, however, can only be partly explained by fetal rest-activity states.     

基于小波神经网络的管道腐蚀缺陷定量识别研究

漏磁检测是目前广泛采用的油气管道检测方法,通过漏磁检测仪获得腐蚀缺陷漏磁场切向分量的漏磁场信号,提取与腐蚀缺陷外形长、宽、深有关的漏磁场信号波形特征量,结合小波分析和神经网络的优势形成小波神经网络,分析设计了小波神经网络的结卡句,给出了网络训练算法,利用网络的非线性逼近性能,对腐蚀缺陷外形进行定量评价,给出预测评价曲线,试验验证方法有效可行。     

Neural Network Computer Analysis of Fetal Heart Rate

> Objective: A nonsubjective evaluation of intrapartum fetal heart rate (FHR) with a neural network (NNW) computer system and its clinical application. Methods: Eight simple FHR data were input into the NNW computer after 16-step normalizations. The computer was composed of 40 units in the input layer, 30 in intermediate layer, and 3 in the output layer, and the probabilities to be normal, suspicious, and pathological were obtained at the output. Before use, the computer was trained 10,000 times by 50-min teacher FHR data of 20 cases with known outcomes. The trained NNW computer was tested by FHRs of another 29 cases. The outcome probabilities in 15 min were calculated every 5 min in another 10 cases, and the bar graphs of the probabilities were displayed in sequence in the trendgrams. Results: The trained NNW computer was 100% accurate in the internal check; in the external check 86% of the results were evaluated correctly with the cardiotocogram, Apgar score, and umbilical arterial pH of the 29 test cases. The FHR scores of our conventional computer FHR analysis were higher in the suspicious and pathological groups than the normal group, and the fetal distress index was high in the pathological group. The trendgrams were simply accurate in typically normal or abnormal cases, transitory abnormal probabilities were shown in intermediate cases, and mixed suspicious and pathological probabilities suggested pathological outcome. Conclusions: The outcome probabilities and their trendgrams in the NNW FHR analysis are promising in objective decision making in the intrapartum stage.     

Discriminant analysis of the Ullrich-Turner syndrome neurocognitive profile

OBJECTIVES: To evaluate the effect of fetal behavioral states on the relationship between fetal heart rate (FHR) and middle cerebral artery resistance index (MCA RI) in normal fetuses. METHODS: The FHR and MCA RI of 10 normal cases from 37 to 40 weeks of gestation were recorded consecutively over a 45-min period. Correlations between the MCA RI and FHR during resting and active phases, classified by an actocardiotocogram, were analyzed by simple regression analysis. RESULTS: The mean FHR and MCA RI were significantly higher during the active phase (140.3 +/- 6.6 bpm, 0.79 +/- 0.06) than those during the resting phase (137.4 +/- 6.8 bpm, 0.75 +/- 0.07, P < 0.01, two sample t-test). There was a significant negative correlation (r = - 0.22, n = 2642, P < 0.01) between RI and FHR during the active phase and a significant positive correlation (r = 0.28, n = 2066, P < 0.001) during the resting phase. CONCLUSIONS: The relationship between FHR and the MCA RI during the resting phase is different from during the active phase.     

Mallat小波快速变换与IDRNN在卫星实时故障检测与识别中的应用

系统研究了面向复杂系统监测时变信号的实时故障检测与识别问题.采用滑窗Mallat小波快速变换克服传统小波变换的时域全局依耐性并提高计算效率,使之适应于实时故障检测;针对时变信号的故障模式识别难题,在故障检测基础上采用改进动态循环神经网络(improved dynamic recurrent neural network,IDRNN)进行智能故障识别.最后将滑动时窗小波检测模块及最优IDRNN网络模块嵌入某型完整卫星姿态控制系统仿真平台进行在线故障诊断.试验结果表明:实时条件下的滑动窗口小波变换与传统小波变换具有一致性,IDRNN对于时变信号识别具有较好的时域泛化能力,将滑窗移动时不变小波方法与IDRNN结合可以实现面向复杂系统监测实时信号的故障检测及复合模式分类.      

Effects of 4 hours magnesium sulfate infusion on fetal heart rate variability and reactivity in a goat model

Effects of magnesium sulfate were investigated on fetal heart rate (FHR) baseline, variability, and reactivity in goats. Six chronically catheterized fetuses of Japanese Saanen goat at 125 to 130 days' gestation (term = 147 days) were used. Magnesium sulfate was directly infused to the fetuses. Short-term variability and long-term variability were obtained according to Huey et al. The baseline, reactivity, short-term variability and long-term variability of the FHR were compared between those receiving magnesium sulfate infusions and those receiving vehicle infusions without magnesium sulfate for 4 hr. Two-way analysis of variance (ANOVA) and Duncan's multiple range test was applied for statistical significance. Four hours magnesium sulfate infusion significantly increased fetal plasma concentration of magnesium from 2.4-6.6 mg/dL, without significant changes in fetal respiratory gases and pH values. The baseline FHR was significantly decreased by magnesium infusion compared with that receiving vehicle infusion. The incidence of acceleration, short-term variability, and long-term variability during the fourth hour of magnesium infusion was also significantly decreased compared to a controlled infusion. The time spent by high amplitude phase of short-term variability and that of long-term variability were also significantly reduced. Significant correlation was obtained between the magnesium concentration and incidence of acceleration at fourth hour of magnesium infusion. Four hours infusion of magnesium sulfate significantly decreases baseline FHR, short-term variability, long-term variability, and reactivity in fetal goats at 0.85 gestation.     

Evaluation of the accuracy of a new ultrasonic fetal heart rate monitor

An indirect Doppler fetal monitoring system has been developed and validated by computer comparison of simultaneous fetal heart rate (FHR) with Doppler and scalp ECG of high-risk patients during labor. The difference in measurement of FHR averaged 0.3 b.p.m., and 93 per cent of the Doppler FHR measures were within 10 b.p.m. of the ECG FHR. If interinstrument difference is discounted, 96 per cent were within 10 b.p.m. All types of decelerations and variability were well approximated. Doppler FHR from the instrument described may be relied upon as valid clinical information and may be obtained from over 90 per cent of labor patients with 93 per cent accuracy.     

小波模糊神经网络在高炉铁水硅质量分数预测中的应用

在运用模糊神经网络进行预测的基础上,建立了一种应用小波理论对时间信号进行去噪,根据去噪处理对模糊神经网络作相应处理的预测模型,并将所建模型应用于预测高炉铁水中硅的质量分数.仿真结果表明小波模糊神经网络比模糊神经网络更具优越性,预测准确率明显提高.     

Digital communication with fetal monitors

BACKGROUND: Fetal heart rate (FHR) values in the averaged format that are provided by commercial computed cardiotocography analysis systems may be unsuitable for special analysis purposes. METHOD: I developed a communication software program to obtain any measured values of fetal monitors for individual analysis of computed cardiotocography. EXPERIENCE: The software program was used to study the data continuity of beat-to-beat FHR values as an experiment for chaos theory and power spectrum analysis. The results indicated that the signal loss was recognized at a precision of 95%. CONCLUSION: The described method of digital communication with fetal monitors was found to be useful for individual purposes in the field of computed cardiotocography analysis.     

Damage Identification Using Committee of Neural Networks

This paper presents a committee of neural networks technique, which employs frequency response functions (FRFs), modal properties (natural frequencies and mode shapes), and wavelet transform (WT) data simultaneously to identify damage in structures. The experimental demonstration of the method is obtained by studying the sensitivities of the FRFs, modal properties, and WT data to four types of faults in a cylindrical shell. The experimental results show that different faults affect data in a different manner. The proposed approach is tested on simulated data from a three-degree-of-freedom mass-spring-damper system. The results from the simulated study show that the performance of the approach is not influenced by the noise in the data. Finally, the method is used to identify damage in a population of ten steel seam-welded cylindrical shells. The proposed method is able to identify damage cases better than the three approaches used individually. The committee approach gives results that generally have a lower mean square error (MSE) than the average MSE of the individual methods. It is found that the effectiveness of the method is enhanced when experimentally measured data are used, which is in contrast to many existing methods. This is because the committee approach assumes that the errors given by the three approaches are uncorrelated, a situation that becomes more apparent when using measured data rather than simulated data.     

小波RBF神经网络在高炉铁水硅含量预测中的应用

在运用RBF神经网络进行预测的基础上,建立了一种应用小波理论对时间信号进行去噪,根据去噪处理对RBF神经网络作相应处理的预测模型,并将所建模型应用于预测高炉铁水硅含量。仿真结果表明小波RBF神经网络比RBF神经网络更具优越性,预测准确率明显提高。     

小波模糊神经网络在高炉炉温预测中的应用

在运用模糊神经网络进行预测的基础上,建立了一种应用小波理论对时间信号进行去噪。根据去噪处理对模糊神经网络作相应处理的预测模型,并将所建模型应用于高炉炉温预测。仿真结果证明小波模糊神经网络比模糊神经网络更具优越性,预测准确率明显提高。     

小波分析在激电数据处理中的应用

激电测量数据中一般包含有噪声和异常叠加效应,如何有效分离出有用异常信息,提高激电异常室内处理的效率和解释水平,成为激电异常解释的重要环节。在数据处理方法上,小波变换是20世纪80年代中期发展起来的一门新兴的数学理论和方法,在空间变换域和频率域同时具有良好的局部分析性质,可以将信号分解成各种不同频率或尺度成分,利用小波变换的上述特点,对激电异常进行划分,便可得到各种尺度意义下的异常。应用小波分析方法对河南省某矿区激电异常进行数据处理,区分出噪声和地表浅小地质体的干扰异常,深源或规模较大地质体的背景异常,突出了矿体的异常信息,取得了较好的效果。     

Simulation of Nonstationary Stochastic Processes by Spectral Representation

This paper presents a rigorous derivation of a previously known formula for simulation of one-dimensional, univariate, nonstationary stochastic processes integrating Priestly’s evolutionary spectral representation theory. Applying this formula, sample functions can be generated with great computational efficiency. The simulated stochastic process is asymptotically Gaussian as the number of terms tends to infinity. This paper shows that (1) these sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic process when the number of terms in the cosine series is large, i.e., the ensemble averaged evolutionary power spectral density function (PSDF) or autocorrelation function approaches the corresponding target function as the sample size increases, and (2) the simulation formula, under certain conditions, can be reduced to that for nonstationary white noise process or Shinozuka’s spectral representation of stationary process. In addition to derivation of simulation formula, three methods are developed in this paper to estimate the evolutionary PSDF of a given time-history data by means of the short-time Fourier transform (STFT), the wavelet transform (WT), and the Hilbert-Huang transform (HHT). A comparison of the PSDF of the well-known El Centro earthquake record estimated by these methods shows that the STFT and the WT give similar results, whereas the HHT gives more concentrated energy at certain frequencies. Effectiveness of the proposed simulation formula for nonstationary sample functions is demonstrated by simulating time histories from the estimated evolutionary PSDFs. Mean acceleration spectrum obtained by averaging the spectra of generated time histories are then presented and compared with the target spectrum to demonstrate the usefulness of this method.