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。     

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180 Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs.     

Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization

The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approach in a multi-channel representation space. The mother wavelet is optimized with the criterion of minimum classification error, as estimated from the learning signal set. The method was applied to the classification of six hand movements with recording of the surface EMG from eight locations over the forearm. Misclassification rate in six subjects using the eight channels was (mean ± S.D.) 4.7 ± 3.7% with the proposed approach while it was 11.1 ± 10.0% without wavelet optimization (Daubechies wavelet). The DWT and SVM can be implemented with fast algorithms, thus, the method is suitable for real-time implementation.     

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。     

Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform

Classification and subsequent diagnosis of cardiac arrhythmias is an important research topic in clinical practice. Confirmation of the type of arrhythmia at an early stage is critical for reducing the risk and occurrence of cardiovascular events. Nevertheless, diagnoses must be confirmed by a combination of specialist experience and electrocardiogram (ECG) examination, which can lead to delays in diagnosis. To overcome such obstacles, this study proposes an automatic ECG classification algorithm based on transfer learning and continuous wavelet transform (CWT). The transfer learning method is able to transfer the domain knowledge and features of images to a EGG, which is a one-dimensional signal when a convolutional neural network (CNN) is used for classification. Meanwhile, CWT is used to convert a one-dimensional ECG signal into a two-dimensional signal map consisting of time-frequency components. Considering that morphological features can be helpful in arrhythmia classification, eight features related to the R peak of an ECG signal are proposed. These auxiliary features are integrated with the features extracted by the CNN and then fed into the fully linked arrhythmia classification layer. The CNN developed in this study can also be used for bird activity detection. The classification experiments were performed after converting the two types of audio files containing songbird sounds and those without songbird sounds from the NIPS4Bplus bird song dataset into the Mel spectrum. Compared to the most recent methods in the same field, the classification results improved accuracy and recognition by 11.67% and 11.57%, respectively.     

Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque--a study using the continuous wavelet transform.

The continuous wavelet transform (CWT), a time-frequency method, was used when calculating mean frequency of the power spectrum (MNF) and signal amplitude (RMS) of the surface EMG to investigate their relationships to force during a gradually increasing knee extension (ramp). Based upon the CWT, MNF was redefined to include time dependence on the EMG signal frequency contents, the short-time MNF (STMNF). Surface EMG was recorded from vastus lateralis, rectus femoris and vastus medialis in 21 clinically healthy subjects during a brief, gradually increasing contraction up to 100% of a maximum voluntary contraction (MVC), with a duration of approximately 10 s. The relationships between the EMG variables and force using linear regression were determined for each subject. For vastus lateralis, we also investigated if certain aspects of the muscle morphology (i.e., proportions and areas of different fibre types) influenced the EMG-force relationship.For the majority of subjects (17-18 out of 21 subjects) there were significant positive correlations between STMNF and force in the three muscles. No sex differences were found in intercepts or regression coefficients of STMNF. The muscle morphology had a significant influence on the STMNF-force intercept and the regression coefficient. Positive and highly significant linear correlations between RMS and force were found for all subjects and all three muscles.In conclusion, time frequency methods can be applied when investigating EMG during brief contractions associated with non-stationarity. In a great majority of the subjects, and in the three muscles, significant linear force dependencies were found for STMNF. Thus, when evaluating muscle fatigue, e.g., in ergonomic situations, it is important to consider the force level as one factor that can influence the results. Morphological variables (fibre proportions and fibre areas) influenced the STMNF-force relationship in vastus lateralis.     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

Predicting allergenic proteins using wavelet transform

MOTIVATION: With many transgenic proteins introduced today, the ability to predict their potential allergenicity has become an important issue. Previous studies were based on either sequence similarity or the protein motifs identified from known allergen databases. The similarity-based approaches, although being able to produce high recalls, usually have low prediction precisions. Previous motif-based approaches have been shown to be able to improve the precisions on cross-validation experiments. In this study, a system that combines the advantages of similarity-based and motif-based prediction is described. RESULTS: The new prediction system uses a clustering algorithm that groups the known allergenic proteins into clusters. Proteins within each cluster are assumed to carry one or more common motifs. After a multiple sequence alignment, proteins in each cluster go through a wavelet analysis program whereby conserved motifs will be identified. A hidden Markov model (HMM) profile will then be prepared for each identified motif. The allergens that do not appear to carry detectable allergen motifs will be saved in a small database. The allergenicity of an unknown protein may be predicted by comparing it against the HMM profiles, and, if no matching profiles are found, against the small allergen database by BLASTP. Over 70% of recall and over 90% of precision were observed using cross-validation experiments. Using the entire Swiss-Prot as the query, we predicted about 2000 potential allergens. AVAILABILITY: The software is available upon request from the authors.     

ERG signal analysis using wavelet transform

The wavelet analysis is a powerful tool for analyzing and detecting features of signals characterized by time-dependent statistical properties, as biomedical signals. The identification and the analysis of the components of these signals in the time–frequency domain, give meaningful information about the physiological mechanisms that govern them. This article presents the results of the wavelet analysis applied to the a-wave component of the human electroretinogram. In order to deepen and improve our knowledge about the behavior of the early photoreceptoral response, including the possible activation of interactions and correlations among the photoreceptors, we have detected and identified the stable time–frequency components of the a-wave, using six representative values of luminance. The results indicate the occurrence of three frequencies lying in the range 20–200 Hz. The lowest one is attributed to the summed activities of the photoreceptors. The others are weaker and at low luminance one of them does not occur. We relate them to the response of the rods and the cones whose aggregate activities are non-linear and typically exhibit self-organization under selective stimuli. The identification of the stable frequency components and of their times of occurrence helps us to shine light about the complex mechanisms governing the a-wave. The present results are promising toward the assessment of more refined model concerning the photoreceptoral activities.     

Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG

A method to detect automatically the location of innervation zones (IZs) from 16-channel surface EMG (sEMG) recordings from the external anal sphincter (EAS) muscle is presented in order to guide episiotomy during child delivery. The new algorithm (2DCorr) is applied to individual motor unit action potential (MUAP) templates and is based on bidimensional cross correlation between the interpolated image of each MUAP template and two images obtained by flipping upside-down (around a horizontal axis) and left–right (around a vertical axis) the original one. The method was tested on 640 simulated MUAP templates of the sphincter muscle and compared with previously developed algorithms (Radon Transform, RT; Template Match, TM). Experimental signals were detected from the EAS of 150 subjects using an intra-anal probe with 16 equally spaced circumferential electrodes. The results of the three algorithms were compared with the actual IZ location (simulated signal) and with IZ location provided by visual analysis (VA) (experimental signals). For simulated signals, the inter quartile error range (IQR) between the estimated and the actual locations of the IZ was 0.20, 0.23, 0.42, and 2.32 interelectrode distances (IED) for the VA, 2DCorr, RT and TM methods respectively.     

基于空间小波变换的生态地理界线识别与定位

为了提高生态地理分界线识别和定位的客观性,探讨了通过空间小波变换获取多尺度模极大值定位过渡带的方法.以NDVI和降水作为小波多尺度分解的对象,应用db3小波核函数分别对49条样带的模极大值进行了多尺度检测,并在GIS中确定其地理坐标.研究结果表明:识别半干旱半湿润生态地理分界线的最佳空间尺度为20～40 km,小于这一尺度定位过程容易受到局部地表覆被因素如城市区域或地形的影响,大于这一尺度由于要素被过度平滑,造成定位不准;从定位点的聚集度分析,NDVI的定位效果好于降水,特别是在较大空间尺度上.而与综合自然地理区划方案中的半干旱半湿润分界线比较,从定位点的方向性、平均最短距离以及均衡度三项指标综合判断,小波变换对于降水过渡带的定位优于对NDVI的定位.研究证实,空间小变换与GIS结合是提高生态地理分界线识别与定位科学性的重要途径,是对专家系统划分界线方法的有力补充和完善.     

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.     

Fatigue compensation during FES using surface EMG

Muscle fatigue limits the effectiveness of FES when applied to regain functional movements in spinal cord injured (SCI) individuals. The stimulation intensity must be manually increased to provide more force output to compensate for the decreasing muscle force due to fatigue. An artificial neural network (ANN) system was designed to compensate for muscle fatigue during functional electrical stimulation (FES) by maintaining a constant joint angle. Surface electromyography signals (EMG) from electrically stimulated muscles were used to determine when to increase the stimulation intensity when the muscle’s output started to drop.

In two separate experiments on able-bodied subjects seated in hard back chairs, electrical stimulation was continuously applied to fatigue either the biceps (during elbow flexion) or the quadriceps muscle (during leg extension) while recording the surface EMG. An ANN system was created using processed surface EMG as the input, and a discrete fatigue compensation control signal, indicating when to increase the stimulation current, as the output. In order to provide training examples and test the systems’ performance, the stimulation current amplitude was manually increased to maintain constant joint angles. Manual stimulation amplitude increases were required upon observing a significant decrease in the joint angle. The goal of the ANN system was to generate fatigue compensation control signals in an attempt to maintain a constant joint angle.

On average, the systems could correctly predict 78.5% of the instances at which a stimulation increase was required to maintain the joint angle. The performance of these ANN systems demonstrates the feasibility of using surface EMG feedback in an FES control system.     

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.     

Comparison of forms of "unfit histograms" by a wavelet transform method

Two methods checking changes in time of histogram patterns were investigated. These methods are relied on discrete wavelet transform. The result is that in the some cases these methods can more effectively to examine latency in the behavior of histograms form constructed by on overlapping short segments of experimental data.     

Digital filter design for peak detection of surface EMG.

A simple Low-Pass Differential (LPD) filter is often used for the Motor Unit Action Potential (MUAP) peak detection in needle EMG decomposition. Decomposition of surface EMG is much more difficult since surface EMG is more mixed than needle EMG. A successful peak detection of MUAPs is a first important step for EMG decomposition. We found that a simple LPD filter is not suitable for surface MUAP detection; instead the Weighted Low-Pass Differential (WLPD) filters are proposed. The filter performance is analyzed based on different window selection and varying MUAPs from simulated and recorded surface EMG. The sinusoidal WLPD filter is found to have better SNR improving factors, and to be more robust under the varying conditions.     

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.     

Pattern recognition of number gestures based on a wireless surface EMG system

Using surface electromyography (sEMG) signal for efficient recognition of hand gestures has attracted increasing attention during the last decade, with most previous work being focused on recognition of upper arm and gross hand movements and some work on the classification of individual finger movements such as finger typing tasks. However, relatively few investigations can be found in the literature for automatic classification of multiple finger movements such as finger number gestures. This paper focuses on the recognition of number gestures based on a 4-channel wireless sEMG system. We investigate the effects of three popular feature types (i.e. Hudgins’ time–domain features (TD), autocorrelation and cross-correlation coefficients (ACCC) and spectral power magnitudes (SPM)) and four popular classification algorithms (i.e. k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM)) in offline recognition. Motivated by the good performance of SVM, we further propose combining the three features and employing a new classification method, multiple kernel learning SVM (MKL-SVM). Real sEMG results from six subjects show that all combinations, except k-NN or LDA using ACCC features, can achieve above 91% average recognition accuracy, and the highest accuracy is 97.93% achieved by the proposed MKL-SVM method using the three feature combination (3F). Referring to the offline recognition results, we also implement a real-time recognition system. Our results show that all six subjects can achieve a real-time recognition accuracy higher than 90%. The number gestures are therefore promising for practical applications such as human–computer interaction (HCI).     

Soft-output signal detection for cetacean vocalizations using spectral entropy,k-means clustering and the continuous wavelet transform

Underwater passive acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in reducing the amount of labor required for signal annotations, which often contain large portions devoid of signals.Cetacean vocalization detection based on spectral entropy is investigated as a means of vocalization discovery. Previous techniques using spectral entropy mostly consider time–frequency enhancement of the entropy measure, and utilize the short time Fourier transform (STFT) as its time–frequency (TF) decomposition. Spectral entropy methods also requires the user to set a detection threshold manually, which call for knowledge of the produced entropy measures.This paper considers median filtering as a simple, effective way to provide temporal stabilization to the entropy measure, and considers the continuous wavelet transform (CWT) as an alternative TF decomposition. K-means clustering is used to determine the threshold required to accurately separate the signal/no-signal entropy measures, resulting in a one-dimensional, two-class classification problem. The class means are used to perform pseudo-probabilistic soft class assignment, which is a useful metric in algorithmic development. The effect of median filtering, signal-to-noise ratio and the chosen TF decomposition are investigated.The accuracy and specificity measures of the proposed detection technique are simulated using a pulsed frequency modulated sweep, corrupted by a sample of ocean noise. The results show that median filtering is particularly effective for low signal-to-noise ratios. Both the STFT and CWT prove to be effective TF analyses for signal detection purposes, each presenting with different advantages and drawbacks. The simulated results provide insight into configuring the proposed detector, which is compared to a conventional STFT-based spectral entropy detector using manually annotated humpback whale (Megaptera novaeangliae) songs recorded in False Bay, South Africa, July2021.The proposed method shows a significant improvement in detection accuracy and specificity, while also providing a more interpretable detection threshold setting via soft class assignment, providing a detector for use in development of adaptive algorithms.