Electrocardiogram Data Compression Techniques for Cardiac Healthcare Systems: A Methodological Review

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues.Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database.Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures.Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.     

Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis

BackgroundFrequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques.MethodIn this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification.ResultsAn experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods.ConclusionAchieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.     

Ecological application of wavelet analysis in the scaling of spatial distribution patterns of Ceratoides ewersmanniana

Ecological experiments are usually conducted on small scales, but the ecological and environmental issues are usually on large scales. Hence, there is a clear need of scaling. Namely, when we deal with patterns and processes on larger scales, a special connection needs to be established on the small scales that we are familiar with. Here we presented a wavelet analysis method that could build relationships between spatial distribution patterns on different scales. With this method, we also studied how spatial heterogeneity and distribution patterns changed with the scale. We investigated the distribution and the habitat of C. ewersmanniana in two plots (200 m × 200 m; the distance between these two plots is 15 km) at Mosuowan desert. The results demonstrated that spatial heterogeneity and distribution patterns were incorporated into larger scales when the wavelet scale varied from one (5 m) to four (20 m). However, if the wavelet scale was above five (25 m), the spatial distribution patterns varied placidly, the oscillation frequency of landforms stabilized at 110 m, and the dynamic quantity period of C. ewersmanniana stabilized at 115–125 m. We also identified signal mutation points with wavelet analysis and verified the heterogeneity degree of local space with position variance. We found that position variance decomposed the distribution patterns on large scales into small sampling plots, and the position with the largest variance also had the strongest heterogeneity. In a word, the wavelet analysis method could scale-up spatial distribution patterns and habitat heterogeneity. With this method and other methods derived from this one, such as wavelet scale, wavelet variance, position variance and extremely direct-viewing graphs, wavelet analysis could be widely applied in solving the scaling problem in ecological and environmental studies.     

The influences of arboraceous layer on spatial patterns and morphological characteristics of herbaceous layer in an arid plant community

The objectives of this study were to examine the effects of arboraceous layer on the spatial pattern and morphological characteristics of herbaceous layer in Elaeagnus angustifolia–Achnatherum splendens community in Ningxia Hui Autonomous Region, China. The analyses of community composition and structural characteristics as well as the investigation of soil moisture and salinity showed that different life forms of plants differ in the soil depth at which they absorb and utilize soil moisture. Wavelet analysis showed that there were differences between the spatial patterns of A. splendens in the canopy-projected regions and other regions, and the intrinsic scales were detected. The results from the buffer analysis showed that the control of arboraceous layer on the herbaceous layer on the spatial patterns and the morphological characteristics were influenced not only by canopy shading but also by other causes such as distribution patterns of roots as the morphological characteristics did not monotonically change with distance.     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement. This paper briefly introduced the original Resonant Recognition Model (RRM), and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM). The key characteristic of the new model is that it can predict directly the protein-protein interaction from the primary sequence, and the MRRM is more suitable than the RRM for this prediction. The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction. Translated from Journal of Shanghai University (Natural Science), 2006, 12(1): 69–73 [译自: 上海大学学报(自然科学版)]     

Wavelet Analysis of DNA Bending Profiles reveals Structural Constraints on the Evolution of Genomic Sequences

Analyses of genomic DNA sequences have shown in previous works that base pairs are correlated at large distances with scale-invariant statistical properties. We show in the present study that these correlations between nucleotides (letters) result in fact from long-range correlations (LRC) between sequence-dependent DNA structural elements (words) involved in the packaging of DNA in chromatin. Using the wavelet transform technique, we perform a comparative analysis of the DNA text and of the corresponding bending profiles generated with curvature tables based on nucleosome positioning data. This exploration through the optics of the so-called `wavelet transform microscope' reveals a characteristic scale of 100-200 bp that separates two regimes of different LRC. We focus here on the existence of LRC in the small-scale regime ( 200 bp). Analysis of genomes in the three kingdoms reveals that this regime is specifically associated to the presence of nucleosomes. Indeed, small scale LRC are observed in eukaryotic genomes and to a less extent in archaeal genomes, in contrast with their absence in eubacterial genomes. Similarly, this regime is observed in eukaryotic but not in bacterial viral DNA genomes. There is one exception for genomes of Poxviruses, the only animal DNA viruses that do not replicate in the cell nucleus and do not present small scale LRC. Furthermore, no small scale LRC are detected in the genomes of all examined RNA viruses, with one exception in the case of retroviruses. Altogether, these results strongly suggest that small-scale LRC are a signature of the nucleosomal structure. Finally, we discuss possible interpretations of these small-scale LRC in terms of the mechanisms that govern the positioning, the stability and the dynamics of the nucleosomes along the DNA chain. This paper is maily devoted to a pedagogical presentation of the theoretical concepts and physical methods which are well suited to perform a statistical analysis of genomic sequences. We review the results obtained with the so-called wavelet-based multifractal analysis when investigating the DNA sequences of various organisms in the three kingdoms. Some of these results have been announced in B. Audit et al. [1, 2].     

Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally optimize the initial weights of the original model, which improved the insensitivity of the model to the initial weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results for the greenhouse showed that the model training speed, prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.     

近60年秦岭山地旱涝变化规律

随着极端气候变化,山地灾害频发。基于秦岭山地32个气象站点的实测数据,以标准化降水蒸散指数(SPEI)为旱涝量化指标,研究了过去60年秦岭山地旱涝时空变化特征、频率、周期等变化规律,结果表明：(1)1960—2019年,秦岭山地年SPEI指数以0.124/10a的速度下降,其中,90.23%的面积呈显著下降趋势,1.96%的面积呈显著上升趋势,并在1990年发生干旱突变;秦岭北坡的干旱化趋势大于南坡,且高海拔地区干旱化更为明显。(2)突变前秦岭山地湿润比例平均值为36.94%,突变后下降为18.19%;干旱比例由突变前的17.64%急剧上升到突变后的38.19%;突变前30年秦岭山地极端干旱事件、严重干旱事件极少发生,发生频率几乎为0;突变后30年严重干旱和极端干旱事件发生频率增加,秦岭南北坡极端湿润和严重湿润事件近乎销声匿迹。(3)整体上,太阳黑子与秦岭山地旱涝变化以显著负相关关系为主;ENSO事件对秦岭山地的旱涝变化影响较大,在La Nina年易发生洪涝事件,在El Ni1o年易发生干旱事件;在不同时域范围内,海表温度距平(SSTA)对秦岭山地旱涝变化的影响不同：1990年以前,S...     

应用近红外光谱法估测小麦叶片糖氮比

糖氮比能够反映作物碳氮代谢的协调程度,及时、准确地监测糖氮比对于作物氮素营养诊断和调控具有重要意义.本研究以不同年份、品种、施氮水平的小麦大田试验为基础,获取鲜叶和粉末状干叶近红外(NIR)光谱及糖氮比信息,分别运用偏最小二乘法(partial least squares, PLS)、BP神经网络(back propagation neural network, BPNN)和小波神经网络(wavelet neural network, WNN)3种方法建立了小麦叶片糖氮比预测模型,并利用随机选择的样品集对所建模型进行测试和检验.结果表明： 小麦鲜叶光谱模型预测性能不佳;而干叶片预测模型表现了较好的准确性,在1655~2378 nm谱区范围内基于3种方法构建的干叶粉末糖氮比估算模型,其预测均方根误差均低于0.3%,决定系数均高于0.9.比较而言,WNN法表现最佳.总体显示,近红外光谱法可以准确预测小麦叶片糖氮比状况,为科学诊断糖氮比提供了理论基础和技术途径.     

On the difference in the net ecosystem exchange of CO2 between deciduous and evergreen forests in the southeastern United States

The southeastern United States is experiencing a rapid regional increase in the ratio of pine to deciduous forest ecosystems at the same time it is experiencing changes in climate. This study is focused on exploring how these shifts will affect the carbon sink capacity of southeastern US forests, which we show here are among the strongest carbon sinks in the continental United States. Using eight‐year‐long eddy covariance records collected above a hardwood deciduous forest (HW) and a pine plantation (PP) co‐located in North Carolina, USA, we show that the net ecosystem exchange of CO₂ (NEE) was more variable in PP, contributing to variability in the difference in NEE between the two sites (ΔNEE) at a range of timescales, including the interannual timescale. Because the variability in evapotranspiration (ET) was nearly identical across the two sites over a range of timescales, the factors that determined the variability in ΔNEE were dominated by those that tend to decouple NEE from ET. One such factor was water use efficiency, which changed dramatically in response to drought and also tended to increase monotonically in nondrought years (P < 0.001 in PP). Factors that vary over seasonal timescales were strong determinants of the NEE in the HW site; however, seasonality was less important in the PP site, where significant amounts of carbon were assimilated outside of the active season, representing an important advantage of evergreen trees in warm, temperate climates. Additional variability in the fluxes at long‐time scales may be attributable to slowly evolving factors, including canopy structure and increases in dormant season air temperature. Taken together, study results suggest that the carbon sink in the southeastern United States may become more variable in the future, owing to a predicted increase in drought frequency and an increase in the fractional cover of southern pines.