Cell classification by moments and continuous wavelet transform methods

Image processing techniques are bringing new insights to biomedical research. The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features. In this work, a simple rule-based decision tree classifier is developed to classify typical features of mixed cell types investigated by atomic force microscopy (AFM). A combination of continuous wavelet transform (CWT) and moment-based features are extracted from the AFM data to represent that shape information of different cellular objects at multiple resolution levels. The features are shown to be invariant under operations of translation, rotation, and scaling. The features are then used in a simple rule-based classifier to discriminate between anucleate versus nucleate cell types or to distinguish cells from a fibrous environment such as a tissue scaffold or stint. Since each feature has clear physical meaning, the decision rule of this tree classifier is simple, which makes it very suitable for online processing. Experimental results on AFM data confirm that the performance of this classifier is robust and reliable.     

Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms

We propose intelligent methods for classifying three different muscle types, i.e. biceps, frontallis and abductor pollicis brevis muscles, with low computational complexity. For this aim, electromyogram (EMG) signals are recorded and modelled by using an auto-regressive (AR) model. As the size of the EMG signals is usually large, the computational complexity of artificial neural network (ANN) systems drastically increases. Therefore, in the proposed scheme EMG signals are pre-processed by using a wavelet transform and then they are modelled by employing an AR approach. The AR coefficients are used to train and test the ANNs. Experimental results show that the highest achieved classification accuracy is more than 95% in the case of EMG signals pre-processed by wavelet transform. The wavelet transform-based pre-processing significantly increases the performance rates compared to standard multilayer perceptron and general regression neural networks algorithms.     

Automatic detection of surface EMG activation timing using a wavelet transform based method

The problem of the identification of the muscle contraction timing by using surface electromyographic signal is addressed. The timing detection of the muscular activation in dynamic conditions has a real clinical diagnostic impact. Widely used single threshold methods still rely on the experience of the operator in manually setting that threshold. A new approach to detect the muscular activation intervals, that is based on discontinuities detection in the wavelet domain, is proposed. Accuracy and precision of the algorithm were assessed by using a set of simulated signals obtaining values lower than 11.0 and 8.7 ms for biases and standard deviations of the estimation, respectively. Moreover an experimental application of the algorithm was carried out recruiting a population of 10 able-bodied subjects and processing the myoelectric signals recorded from the lower limb during an isokinetic exercise. The algorithm was able to reveal correctly the timing of muscular activation with performance comparable to the state-of-the-art methods. The detection algorithm is automatic and user-independent, it manages the detection of both onset and offset activation, it can be fruitfully applied even in presence of noise and, therefore, it can be used also by unskilled operators.     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

Improved seabird image classification based on dual transfer learning framework and spatial pyramid pooling

Seabird plays an important role in the marine ecosystem and is an indispensable part of the food chain. However, the seabird population has been experiencing a rapid decline due to various factors including climate change, fisheries, and invasive non-native species. To better protect seabirds, the first step is to accurately monitor them. Automatic classification of seabirds would significantly speed up the monitoring process. In this paper, we propose a dual transfer learning framework for improved seabird image classification based on spatial pyramid pooling. Specifically, a dual transfer learning framework is used to capture various patterns to improve the discriminability of the proposed model. Both InceptionV3 and DenseNet201 are used as the backbones, whose outputs are concatenated using a spatial pyramid pooling (SPP) layer. Here, SPP is used to address images of different sizes. Next, two types of pooling, global average-pooling (GAP) and global max-pooling (GMP) are applied to the output of the SPP layer, where the results of GAP and GMP are linearly added up. Our method takes both InceptionV3 and DenseNet201 as feature extractors and is trained offline in an end-to-end style. The proposed dual transfer learning framework-based seabird image classification method reached the accuracy, precision, recall, F1-score of 95.11%, 95.33%, 95.11%, 95.13% on the 10 classes seabird image dataset.     

Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method

Inspection of insect sticky paper traps is an essential task for an effective integrated pest management (IPM) programme. However, identification and counting of the insect pests stuck on the traps is a very cumbersome task. Therefore, an efficient approach is needed to alleviate the problem and to provide timely information on insect pests. In this research, an automatic method for the multi-class recognition of small-size greenhouse insect pests on sticky paper trap images acquired by wireless imaging devices is proposed. The developed algorithm features a cascaded approach that uses a convolutional neural network (CNN) object detector and CNN image classifiers, separately. The object detector was trained for detecting objects in an image, and a CNN classifier was applied to further filter out non-insect objects from the detected objects in the first stage. The obtained insect objects were then further classified into flies (Diptera: Drosophilidae), gnats (Diptera: Sciaridae), thrips (Thysanoptera: Thripidae) and whiteflies (Hemiptera: Aleyrodidae), using a multi-class CNN classifier in the second stage. Advantages of this approach include flexibility in adding more classes to the multi-class insect classifier and sample control strategies to improve classification performance. The algorithm was developed and tested for images taken by multiple wireless imaging devices installed in several greenhouses under natural and variable lighting environments. Based on the testing results from long-term experiments in greenhouses, it was found that the algorithm could achieve average F₁-scores of 0.92 and 0.90 and mean counting accuracies of 0.91 and 0.90, as tested on a separate 6-month image data set and on an image data set from a different greenhouse, respectively. The proposed method in this research resolves important problems for the automated recognition of insect pests and provides instantaneous information of insect pest occurrences in greenhouses, which offers vast potential for developing more efficient IPM strategies in agriculture.     

Protein sequence comparison based on the wavelet transform approach

A protein's chemical properties, the chain conformation, the function of the protein and its species specificity are determined by the information contained in the amino acid sequence. Proteins of similar functions have at some level sequential identical amino acid sequences. The closer the phylogenetic relationship, the more similar are the sequences. To find the similarities between two or more protein sequences is of great importance for protein sequence analysis. The differences in the amino acid sequences permit the construction of a family tree of evolution. In this work, a comparison method was devised that is capable of analysing a protein sequence 'hierarchically', i.e. it can examine a protein sequence at different spatial resolutions. Based on a wavelet decomposition of protein sequences and a cross-correlation study, a sequence-scale similarity concept is proposed for generating a similarity vector, which renders the comparison of two sequences feasible at different spatial resolutions (scales). This new similarity concept is an expansion of the conventional sequence similarity, which only takes into account the local pairwise amino acid match and ignores the information contained in coarser spatial resolutions.     

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.     

Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal

The performance of different wavelet- and wavelet packet-based methods for removing simulated noise was studied using an electrocardiogram (ECG) signal. A non-linear denoising approach was investigated by applying soft and hard thresholding methods, in which thresholds were chosen using four different methods. Coiflet wavelet and wavelet packet functions were used to build up the dyadic wavelet and optimized wavelet packet decompositions. This study involves the quantitative comparison of different denoising approaches by means of optimized error measures and visual inspection of the denoised ECG and the error signal. The localization of the denoising error within the cardiac cycle was studied by visual inspection of the denoised signal and extracting the error measures during the QRS-complex. The results showed that wavelet denoising approaches were generally more efficient than wavelet packet approaches in all cases, but with heuristic sure threshold selection rule as hard thresholding for white noises was used. Denoising errors tend to concentrate within the QRS-area when the wavelet approach was employed. Moreover, soft and hard non-linearities showed different balances in denoising the high-frequency parts of an ECG. Received: 27 April 1998 / Accepted in revised form: 24 November 1998     

基于小波变换的混合二维ECG数据压缩方法

提出了一种新的基于小波变换的混合二维心电(electrocardiogram,ECG)数据压缩方法。基于ECG数据的两种相关性,该方法首先将一维ECG信号转化为二维信号序列。然后对二维序列进行了小波变换,并利用改进的编码方法对变换后的系数进行了压缩编码:即先根据不同系数子带的各自特点和系数子带之间的相似性,改进了等级树集合分裂(setpartitioninghierarchicaltrees,SPIHT)算法和矢量量化(vectorquantization,VQ)算法;再利用改进后的SPIHT与VQ相混合的算法对小波变换后的系数进行了编码。利用所提算法与已有具有代表性的基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明:所提算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比。     

Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform

The automatic segmentation of cardiac sound signals into heart beat cycles is generally required for the diagnosis of heart valve disorders. In this paper, a new method for segmentation of the cardiac sound signals using tunable-Q wavelet transform (TQWT) has been presented. The murmurs from cardiac sound signals are removed by suitably constraining TQWT based decomposition and reconstruction. The Q-factor, redundancy parameter and number of stages of decomposition of the TQWT are adapted to the desired statistical properties of the murmur-free reconstructed cardiac sound signals. The envelope based on cardiac sound characteristic waveform (CSCW) is extracted after the removal of low energy components from the reconstructed cardiac sound signals. Then the heart beat cycles are derived from the original cardiac sound signals by mapping the required timing information of CSCW which is obtained using established methods. The experimental results are included in order to show the effectiveness of the proposed method for segmentation of cardiac sound signals in comparison with other existing methods for various clinical cases.     

Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. As a result of genome and other sequencing projects, the gap between the number of known apoptosis protein sequences and the number of known apoptosis protein structures is widening rapidly. Because of this extremely unbalanced state, it would be worthwhile to develop a fast and reliable method to identify their subcellular locations so as to gain better insight into their biological functions. In view of this, a new method, in which the support vector machine combines with discrete wavelet transform, has been developed to predict the subcellular location of apoptosis proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method can remarkably improve the prediction accuracy of subcellular locations, and might also become a useful high-throughput tool in characterizing other attributes of proteins, such as enzyme class, membrane protein type, and nuclear receptor subfamily according to their sequences.     

Temporal location of the cardiac rhythm frequency structure based on discrete wavelet transform

Application of discrete wavelet transform to the temporal location of the frequency structure of the cardiac rhythm is considered. A method for analysis of tachograms (TGs) consisting of signal preparation, discrete wavelet transform, and a variant of estimation of the relative contributions of the rhythm frequency components is suggested. The use of the method is exemplified by analysis of a TG containing recordings of transitional processes, including the moments of a fixed respiration rate test, Valsalva test, and orthostatic test. The detected characteristics of changes in the frequency components are demonstrated by comparing them with the changes in the integral index of the total regulatory effect on the sinus node, which forms the response to these functional tests.     

Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains

This paper presents a new ECG denoising approach based on noise reduction algorithms in empirical mode decomposition (EMD) and discrete wavelet transform (DWT) domains. Unlike the conventional EMD based ECG denoising approaches that neglect a number of initial intrinsic mode functions (IMFs) containing the QRS complex as well as noise, we propose to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal. The signal thus obtained is transformed in the DWT domain, where an adaptive soft thresholding based noise reduction algorithm is employed considering the advantageous properties of the DWT compared to that of the EMD in preserving the energy in the presence of noise and in reconstructing the original ECG signal with a better time resolution. Extensive simulations are carried out using the MIT-BIH arrythmia database and the performance of the proposed method is evaluated in terms of several standard metrics. The simulation results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately and consistently in comparison to some of the stateof-the-art methods.     

基于空间连续小波变换和电路模型的宁夏沿黄河城市带生态安全格局构建

生态安全格局的构建有助于减缓生态系统退化和生物多样性丧失,对生物多样性保护具有重要意义。现有研究中生态源地识别的主观性较强且源地边界破碎,生态廊道对景观连通性的贡献程度也有待明确。因此,以宁夏沿黄河城市带为例,首先利用空间连续小波变换识别综合生态系统服务突变点,获取生态源地,进而基于电路模型评估功能连通性以提取生态廊道,最后量化生态廊道的连通贡献。结果显示,生态源地占研究区总面积的41.51%,其中草地占比83.27%。相比于直接提取综合生态系统服务高值区,基于空间连续小波变换识别的生态源地能较好的反映局地生态系统特征,并将景观形状指数和分形维数分别降低67.20%和8.06%,减少生态源地边缘长度从而受到外界干扰的可能性更小。生态廊道占研究区总面积的7.42%,是物种迁徙等生态过程的关键通道,有效地增强了区域景观连通性,将等效可能连通性指数和等效整体连通性指数分别提升41.76%和68.68%。生态安全格局主要位于研究区边界地段,整体呈两带分布,为宁夏沿黄河城市带阻挡周围的沙漠,保障中部的城市发展和粮食安全。然而,研究区范围内自然保护区仍存在较为严重的生态系统服务和功能连通的保护缺口。各区域的生态安全格局具备不同的生态功能,需要有针对性的差异化保护、修复与管理。     

The effects of morphine on the relationship between fetal EEG, breathing and blood pressure signals using fast wavelet transform

In this study, we introduce the fast wavelet transform (WT) as a method for investigating the effects of morphine on the electroencephalogram (EEG), respiratory activity and blood pressure in fetal lambs. Morphine was infused intravenously at 25 mg/h. The EEG, respiratory activity and blood pressure signals were analyzed using WT. We performed wavelet decomposition for five sets of parameters D _2j where -1 < j 5. The five series WTs represent the detail signal bandwidths: 1, 16–32 Hz; 2, 8–16 Hz; 3, 4–8 Hz; 4, 2–4 Hz; 5, 1–2 Hz. Before injection of the high-dose morphine, power in the EEG was high in all six frequency bandwidths. The respiratory and blood pressure signals showed common frequency components with respect to time and were coincident with the low-voltage fast activity (LVFA) EEG signal. Respiratory activity was observed during only some of the LVFA periods, and was completely absent during high-voltage slow activity (HVSA) EEG. The respiratory signal showed dominant power in the fourth wavelet band, and less power in the third and fifth bands. The blood pressure signal was also characterized by dominant power in the fourth wavelet band. This power was significantly increased during periods of respiratory activity. There was a strong relationship between fetal EEG, blood pressure and breathing movements. However, the injection of high-dose morphine resulted in a disruption of the normal cyclic pattern between the two EEG states and a significant increase in power in the first wavelet band. In addition, the high-dose drug resulted in a significant increase in the power of respiratory signal in the fourth and fifth wavelet bands, while power was reduced in the third wavelet band. Breathing activity was also continuous after the drug. The high-dose morphine also caused a temporary power shift from the third wavelet band to the fourth wavelet band for the 30-min period after injection of drug. Finally, high-dose morphine completely destroyed the correlation between EEG, breathing and blood pressure signals.     

A novel estimator for the rate of information transfer by continuous signals

The information transfer rate provides an objective and rigorous way to quantify how much information is being transmitted through a communications channel whose input and output consist of time-varying signals. However, current estimators of information content in continuous signals are typically based on assumptions about the system's linearity and signal statistics, or they require prohibitive amounts of data. Here we present a novel information rate estimator without these limitations that is also optimized for computational efficiency. We validate the method with a simulated Gaussian information channel and demonstrate its performance with two example applications. Information transfer between the input and output signals of a nonlinear system is analyzed using a sensory receptor neuron as the model system. Then, a climate data set is analyzed to demonstrate that the method can be applied to a system based on two outputs generated by interrelated random processes. These analyses also demonstrate that the new method offers consistent performance in situations where classical methods fail. In addition to these examples, the method is applicable to a wide range of continuous time series commonly observed in the natural sciences, economics and engineering.     

Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory

The coupling between G protein-coupled receptors (GPCRs) and guanine nucleotide-binding proteins (G proteins) regulates various signal transductions from extracellular space into the cell. However, the coupling mechanism between GPCRs and G proteins is still unknown, and experimental determination of their coupling specificity and function is both expensive and time consuming. Therefore, it is significant to develop a theoretical method to predict the coupling specificity between GPCRs and G proteins as well as their function using their primary sequences. In this study, a novel four-layer predictor (GPCRsG_CWTIT) based on support vector machine (SVM), continuous wavelet transform (CWT) and information theory (IT) is developed to classify G proteins and predict the coupling specificity between GPCRs and G proteins. SVM is used for construction of models. CWT and IT are used to characterize the primary structure of protein. Performance of GPCRsG_CWTIT is evaluated with cross-validation test on various working dataset. The overall accuracy of the G proteins at the levels of class and family is 98.23 and 85.42%, respectively. The accuracy of the coupling specificity prediction varies from 74.60 to 94.30%. These results indicate that the proposed predictor is an effective and feasible tool to predict the coupling specificity between GPCRs and G proteins as well as their functions using only the protein full sequence. The establishment of such an accurate prediction method will facilitate drug discovery by improving the ability to identify and predict protein-protein interactions. GPCRsG_CWTIT and dataset can be acquired freely on request from the authors.