数字信号处理在生物医学工程中的应用

数字信号处理技术一诞生就显示了强大的生命力,展现了极为广阔的应用前景.主要讨论数字信号处理技术中小波分析、人工神经网络、维格纳分布在生物医学工程中的应用,并对数字信号处理技术在生物医学工程中的应用前景进行了展望.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

The annual cycles of phytoplankton biomass

Terrestrial plants are powerful climate sentinels because their annual cycles of growth, reproduction and senescence are finely tuned to the annual climate cycle having a period of one year. Consistency in the seasonal phasing of terrestrial plant activity provides a relatively low-noise background from which phenological shifts can be detected and attributed to climate change. Here, we ask whether phytoplankton biomass also fluctuates over a consistent annual cycle in lake, estuarine–coastal and ocean ecosystems and whether there is a characteristic phenology of phytoplankton as a consistent phase and amplitude of variability. We compiled 125 time series of phytoplankton biomass (chlorophyll a concentration) from temperate and subtropical zones and used wavelet analysis to extract their dominant periods of variability and the recurrence strength at those periods. Fewer than half (48%) of the series had a dominant 12-month period of variability, commonly expressed as the canonical spring-bloom pattern. About 20 per cent had a dominant six-month period of variability, commonly expressed as the spring and autumn or winter and summer blooms of temperate lakes and oceans. These annual patterns varied in recurrence strength across sites, and did not persist over the full series duration at some sites. About a third of the series had no component of variability at either the six- or 12-month period, reflecting a series of irregular pulses of biomass. These findings show that there is high variability of annual phytoplankton cycles across ecosystems, and that climate-driven annual cycles can be obscured by other drivers of population variability, including human disturbance, aperiodic weather events and strong trophic coupling between phytoplankton and their consumers. Regulation of phytoplankton biomass by multiple processes operating at multiple time scales adds complexity to the challenge of detecting climate-driven trends in aquatic ecosystems where the noise to signal ratio is high.     

Nitrous oxide fluxes over establishing biofuel crops: Characterization of temporal variability using the cross‐wavelet analysis

Emissions of nitrous oxide (N₂O) over croplands are a major source of greenhouse gases to the atmosphere. The precise accounting of sources of N₂O is essential to national and global budgets, as well as the understanding of the spatial and temporal relationships with environmental variables such as rainfall, air and soil temperature, and soil moisture. The objective of this work was to investigate the temporal correlations of N₂O fluxes with soil and air temperatures, as well as soil moisture. N₂O fluxes were measured over four biofuel crops in Central Illinois during their establishment phase. Measurements were carried out from 2009 to 2011 using a trace gas analyzer (TGA) with tunable laser technology. Measurements of concentrations of N₂O and CO₂ were taken at the center of four plots of maize/soybean rotation, miscanthus (Miscanthus × giganteus), switchgrass (Panicum virgatum) and a mixture of native prairie plants. Cumulative fluxes indicate an average emission of nitrogen via N₂O fluxes on the order of 1.5 kg N ha^?1 year^?1, in agreement with chamber measurements previously reported for the site. N₂O fluxes were associated with peaks in soil and air temperature, and soil moisture, particularly during spring and winter thaws. Cross‐wavelet analysis was used to investigate the correlation between N₂O fluxes and those variables. Results indicate that N₂O fluxes and meteorological variables have significant covariance in time scales ranging from 4 to 32 days. In addition, temporal delays of 1–8 days were found in those relationships. Cross‐wavelet patterns were similar when relating N₂O fluxes with soil temperature, air temperature and soil moisture. The temporal patterns of fluxes and environmental variables reported here support the modeling of emissions and highlight the importance of considering the timing of fluxes in relation to trends in meteorological variables.     

Prior exposure to stress allows the maintenance of an ecosystem cycle following severe acidification

Ecosystem processes vary temporally due to environmental fluctuations, such as when variation in solar energy causes diurnal cycles in primary production. This normal variation in ecosystem functioning may be disrupted and even lost if taxa contributing to functioning go extinct due to environmental stress. However, when communities are exposed to the stress at sub-lethal levels over several generations, they may be able to develop community-level stress tolerance via ecological (e.g. species sorting) or evolutionary (e.g. selection for tolerant genotypes) mechanisms and thus avoid the loss of stability, as defined by the resistance of a process. Community tolerance to a novel stressor is expected to increase the resistance of key processes in stressed ecosystems. In freshwater communities we tested whether prolonged prior exposure to an environmental stressor, i.e. acidification, could increase ecosystem stability when the communities were exposed to a subsequent press perturbation of more severe acidification. As a measure of ecosystem stability, we quantified the diurnal variation in dissolved oxygen (DO), and the resistance of the DO cycle and phytoplankton biomass. High-frequency data from oxygen loggers deployed in 12 mesocosms showed that severe acidification with sulfuric acid to pH 3 could cause a temporary (i.e. two-week long) loss of diurnal variation in dissolved oxygen concentration. The loss of diurnal variation was accompanied by a strong reduction in phytoplankton biomass. However, the pre-exposure to acidification for several weeks resulted in the maintenance of the diurnal cycle and higher levels of phytoplankton biomass, though they did not return to as rapidly to pre-exposure functioning as non-exposed mesocosms. These results suggest that ecosystem stability is intrinsically linked to community-wide stress tolerance, and that a history of exposure to the stressor may increase resistance to it, though at the cost of some resilience.     

Changes in electromyographic activity after botulinum toxin injection of the rectus femoris in patients with hemiparesis walking with a stiff-knee gait

PurposeThis study was designed to evaluate the effects of botulinum toxin type-A (BoNTA) injection of the rectus femoris (RF) muscle on the electromyographic activity of the knee flexor and extensor and on knee and hip kinematics during gait in patients with hemiparesis exhibiting a stiff-knee gait.MethodTwo gait analyses were performed on fourteen patients: before and four weeks after BoNTA injection. Spatiotemporal, kinematic and electromyographic parameters were quantified for the paretic limb.ResultsBoNTA treatment improved gait velocity, stride length and cadence with an increase of knee angular velocity at toe-off and maximal knee flexion in the swing phase. Amplitude and activation time of the RF and co-activation duration between the RF and biceps femoris were significantly decreased. The instantaneous mean frequency of RF was predominantly lower in the pre-swing phase.ConclusionsThe results clearly show that BoNTA modified the EMG amplitude and frequency of the injected muscle (RF) but not of the synergist and antagonist muscles. The reduction in RF activation frequency could be related to increased activity of slow fibers. The frequency analysis of EMG signals during gait appears to be a relevant method for the evaluation of the effects of BoNTA in the injected muscle.     

Textural characterization of histopathological images for oral sub-mucous fibrosis detection

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The aim of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, a systematic approach is introduced in order to grade the histopathological tissue sections into normal, OSF without dysplasia and OSF with dysplasia, which would help the oral onco-pathologists to screen the subjects rapidly. In totality, 71 textural features are extracted from epithelial region of the tissue sections using various wavelet families, Gabor-wavelet, local binary pattern, fractal dimension and Brownian motion curve, followed by preprocessing and segmentation. Wavelet families contribute a common set of 9 features, out of which 8 are significant and other 61 out of 62 obtained from the rest of the extractors are also statistically significant (p < 0.05) in discriminating the three stages. Based on mean distance criteria, the best wavelet family (i.e., biorthogonal3.1 (bior3.1)) is selected for classifier design. support vector machine (SVM) is trained by 146 samples based on 69 textural features and its classification accuracy is computed for each of the combinations of wavelet family and rest of the extractors. Finally, it has been investigated that bior3.1 wavelet coefficients leads to higher accuracy (88.38%) in combination with LBP and Gabor wavelet features through three-fold cross validation. Results are shown and discussed in detail. It is shown that combining more than one texture measure instead of using just one might improve the overall accuracy.     

基于小波变换的土壤有机质含量高光谱估测

利用统计分析方法选取了土壤N、P、K元素含量近似而有机质含量差异较大的样本60个,通过高光谱探测分析获得样本反射率对数的一阶导数光谱,采用Bior 1.3函数进行多层离散小波分解,剔除低频近似信号和高频噪声信号,得到反映土壤理化参数的特征光谱曲线;采用相关分析筛选土壤有机质含量的显著相关波段,基于显著相关波段和特征光谱曲线分别构建土壤有机质含量高光谱多元回归估测模型;通过比较分析,确定了提取土壤有机质特征光谱的最佳小波分解尺度并构建了最佳预测模型.结果表明： 提取土壤有机质特征光谱的最佳小波分解层数是9层,其次是8层和10层;基于小波9层分解特征光谱曲线的有机质含量估测模型最佳,其决定系数（R²）为0.89,比基于显著相关波段构建模型的R²增加了0.31,比基于原始光谱所构建模型的R²增加了0.10.     

基于小波变换的土壤有机质含量高光谱估测术

利用统计分析方法选取了土壤N、P、K元素含量近似而有机质含量差异较大的样本60个,通过高光谱探测分析获得样本反射率对数的一阶导数光谱,采用Bior 1.3函数进行多层离散小波分解,剔除低频近似信号和高频噪声信号,得到反映土壤理化参数的特征光谱曲线;采用相关分析筛选土壤有机质含量的显著相关波段,基于显著相关波段和特征光谱曲线分别构建土壤有机质含量高光谱多元回归估测模型;通过比较分析,确定了提取土壤有机质特征光谱的最佳小波分解尺度并构建了最佳预测模型.结果表明:提取土壤有机质特征光谱的最佳小波分解层数是9层,其次是8层和10层;基于小波9层分解特征光谱曲线的有机质含量估测模型最佳,其决定系数(R2)为0.89,比基于显著相关波段构建模型的R2增加了0.31,比基于原始光谱所构建模型的R2增加了0.10.