提高H2O2/甲酸体系选择性氧化抽提脱硫效率的研究

为了降低焦化蜡油(CGO)-甲酸／H2O2选择性氧化体系中H2O2的无效分解速度，屏蔽重金属离子对H2O2分解的催化效应，考察了造纸行业中常用的硅酸钠、硅酸镁和乙二胺四乙酸(EDTA)等稳定剂和螯合剂的加入对有机溶剂抽提后CGO中含硫量的影响。实验结果表明，EDTA可以很好地和重金属离子螯合，氧化后CGO抽余油中硫含量大幅降低。     

表面改性在介入器械上的应用

论述赋予介入器械的材料表面润滑性、抗菌性、抗凝血性及抗组织增生等性能的各种表面改性技术的现状，讨论这些技术的一些应用的实例，提出需要进一步研究的问题。     

超声多普勒管壁搏动和血流信号的分离研究

本研究提出利用经验模式分解(EMD)算法分解混叠有管壁成分的超声多普勒血流信号来实现管壁搏动和血流信号的分离。该方法首先将混叠有管壁搏动的超声多普勒血流信号分解为少量有限的分量,即内模函数(IMFs),然后根据管壁搏动信号与血流信号的功率比变化曲线,用比值法自动确定并去除低频管壁博动成分。在仿真实验中用提出的方法处理模拟的多普勒信号,对于靠近管腔内壁的血流信号其在频域功率谱上的相对误差为50%,在时域幅度的相对误差为45%,与高通滤波器方法的相对误差95%相比,准确性得到提高。基于个人计算机用C语言编程实现提出的算法,对实际采集的人体颈动脉多普勒信号可实现实时分离处理。结果表明:基于经验模式分解的滤波方法能有效客观地滤除管壁搏动信号,更准确地保留低频血流信号成分。     

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.     

Correlation-based decomposition of surface electromyograms at low contraction forces

The paper studies a surface electromyogram (SEMG) decomposition technique suitable for identification of complete motor unit (MU) firing patterns and their motor unit action potentials (MUAPs) during low-level isometric voluntary muscle contractions. The algorithm was based on a correlation matrix of measurements, assumed unsynchronised (uncorrelated) MU firings, exhibited a very low computational complexity and resolved the superimposition of MUAPs. A separation index was defined that identified the time instants of an MU's activation and was eventually used for reconstruction of a complete MU innervation pulse train. In contrast with other decomposition techniques, the proposed approach worked well also when the number of active MUs was slightly underestimated, if the MU firing patterns partly overlapped and if the measurements were noisy. The results on synthetic SEMG show 100% accuracy in the detection of innervation pulses down to a signal-to-noise ratio (SNR) of 10 dB, and 93±4.6% (mean± standard deviation) accuracy with 0 dB additive noise. In the case of real SEMG, recorded with an array of 61 electrodes from biceps brachii of five subjects at 10% maximum voluntary contraction, seven active MUs with a mean firing rate of 14.1 Hz were identified on average.     

基于稀疏分解法的单次诱发电位提取

迭代加权稀疏分解法是按照白噪声在小波的多分辨结构中的二尺度关系来确定求最小l1模优化问题时的加权系数,并通过一个迭代过程来逐步消除强噪声的影响。通过对视觉诱发电位的单次提取的研究说明了这种方法具有良好的单次提取效果,其实验结果支持单次提取的视觉诱发电位是不相同的观点。     

基于小波分解的多尺度医学图像融合技术

给出了一种基于小波分解的多尺度图像融合新方法。其基本思想是 ,先对源图像进行小波多尺度分解 ;其次 ,采用了基于区域特性量测选择的加权算子的融合规则进行小波系数融合 ;最后通过小波逆变换重构融合图像。实验结果表明 ,该融合方法十分有效 ,融合图像完好地显示了源图像各自的信息。     

基于经验模式分解的心电特征提取算法

本研究应用基于经验模式分解的心电特征提取方法,利用第一本征模函数(intrinsic mode function,IMF)分量对QRS波进行定位,并通过减少分解层数、筛选次数、处理区域等策略实现了快速算法。利用MIT-BIT心律失常数据库的数据进行算法测试,取得较高的检测率,检测速度也有明显提高。实验结果表明,经验模式分解算法在QRS波定位中具有相当的优越性,临床应用中取得了良好的检测效果。     

Adaptive motor unit action potential clustering using shape and temporal information

An adaptive algorithm is described that groups motor unit action potentials (MUAPs), detected in a composite EMG signal during signal decomposition, and creates partial motor unit action potential trains (MUAPTs). Data-driven MUAP shape and motor unit firing-pattern based criteria are used to form the clusters. An algorithm for estimating MUAPT temporal parameter, which provides accurate estimates even for partially defined trains, is used to obtain firing-pattern information. No a priori knowledge is required regarding the number of clusters or the distribution of their template shapes. The clustering algorithm when applied to real concentric-needle detected MUAP data provides accurate and useful clustering results. Compared to a classical leader-based algorithm, it provides more robust performance, is better able to estimate the true number of motor units represented in a set of detected MUAPs, and obtains more complete and accurate MUAPTs.     

Unrestricted principal components analysis of brain electrical activity: Issues of data dimensionality,artifact, and utility

Summary Principal components analysis (PCA) was performed on the 1536 spectral and 2944 evoked potential (EP) variables generated by neurophysiologic paradigms including flash VER, click AER, and eyes open and closed spectral EEG from 202 healthy subjects aged 30 to 80. In each case data dimensionality of 1500 to 3000 was substantially reduced using PCA by magnitudes of 20 to over 200. Just 20 PCA factors accounted for 70% to 85% of the variance. Visual inspection of the topographic distribution of factor loading scores revealed complex loadings across multiple data dimensions (time-space and frequency-space). Forty-two non-artifactual factors were successful in classifying age, gender, and a separate group of 60 demented patients by linear discriminant analysis. Discrimination of age and gender primarily involved EP derived factors, whereas dementia primarily involved EEG derived factors. Thirty-eight artifactual factors were identified which, alone, could not discriminate age but were relatively successful in discriminating gender and dementia. The need to parsimoniously develop real neurophysiologic measures and to objectively exclude artifact are discussed. Unrestricted PCA is suggested as a step in this direction.Acknowledgements: This work was supported in part by NIA program project PO1AG049853 to M. Albert and the Mental Retardation Program Project P30HD18655 to J.J. Volpe. We thank our qEEG technologists Adele Mirabella, Susan Katz, Ellen Belles, and Marianne McGaffigan as well as our research secretaries for their unflagging support.