Wavelet Packet Analysis Research Articles

A multichannel time–frequency and multi-wavelet toolbox for uterine electromyography processing and visualisation

The uterine electromyogram, also called electrohysterogram (EHG), is an electrical signal generated by the uterine contractile activity. The EHG has been considered a promising biomarker for labour and preterm labour prediction, for which there is a demand for accurate estimation methods. Preterm labour is a significant public health concern and one of the major causes of neonatal mortality and morbidity [1]. Given the non-stationary properties of the EHG signal, time–frequency domain analysis can be used. For real life signals it is not generally possible to determine a priori the suitable quadratic time–frequency kernel or the appropriate wavelet family and relative parameters, regarding, for instance, the adequate detection of the signal frequency variation in time. There has been a lack of a comprehensive software tool for the selection of the appropriate time frequency representation of a multichannel EHG signal and extraction of relevant spectral and temporal information. The presented toolbox (Uterine Explorer) has been specifically designed for the EHG analysis and exploration in view of the characterisation of its components. The starting point is the multichannel scalogram or spectrogram representation from which frequency and time marginals, instantaneous frequency and bandwidth are obtained as EHG features. From this point the detected components undergo parametric and non-parametric spectral estimation and wavelet packet analysis. Intrauterine pressure estimation (IUP) is obtained using the Teager, RMS, wavelet marginal and Hilbert operators over the EHG. This toolbox has been tested to build up a dictionary of 288 EHG components [2], useful for research in preterm labour prediction.

Image classification based on ICA-WP feature of EEG signal

In this paper, a method for classifying electroencephalographic (EEG) recordings with images as stimulation is introduced, which aims at selecting the target images. EEG recordings to be processed are referred to the onset of the test images with a single stimulation so as to avoid spending extra time on repeating images. Independent component analysis (ICA) is used to reduce the redundancy of EEG recordings, and wavelet packet (WP) analysis is efficient for dealing with the non-stationary character of brain activity. Feature vectors are extracted by a method that combines these two algorithms. The support vector machine is used as a classifier, carrying out the classification result. The experimental results demonstrate that the accuracy of this method's image classification is affected very little by different classifier parameters. The best result achieves 90% accuracy, which indicts it is feasible for classifying images with a single stimulation.

An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

Support Vector Machine (SVM) classifier is widely used in analogue circuit diagnosis. However, the penalty parameter C and the kernel parameter γ of SVM classifier with the radial basis function (RBF) affect the classification performance seriously. A double-chains-quantum-genetic-algorithm (DCQGA) based method is proposed to optimize C and γ. In DCQGA, each chromosome carries two gene chains, and each of gene chains represents an optimization solution, which can accelerate the search process and help to find the global solution. Thereafter, the optimal parameters C and γ are obtained by optimizing the parameter searching process with DCQGA. Two common datasets named Iris and Wine from UCI Machine Learning Repository are used to test the performance of the presented SVM classifier. The simulation results illustrate that the population's best fitness and the classifying accuracy of the proposed DCQGA-SVM are higher than that of the Particle-Swarm-Optimization based SVM (PSO-SVM), the Quantum Genetic Algorithm based SVM (QGA-SVM) and the classifier based on grid search method (GS-SVM). Finally, the proposed DCQGA-SVM is applied to analogue circuit diagnosis, a Sallen–Key bandpass filter circuit and a four-opamp biquad high-pass filter are chosen as circuits under test (CUT). Wavelet packet analysis is performed to extract the fault features before classifying. The experimental results show that the SVM parameters selected by DCQGA-SVM contribute to higher diagnosis accuracy than other methods referred in this paper.

Data Denoising and Compression for Smart Grid Communication

A technique based on wavelet packet decomposition (WPD) is proposed for the analysis, denoising, and compression of power system data in the smart grid (SG) communication. WPD is an expansion of wavelet decomposition (WD) tree algorithm to a full binary tree. The main advantage of WPD is better signal representation by finding the best tree from a number of bases of the WPD. Thus, the wavelet packet analysis provides satisfactory preservation of feature integrity and removal of redundancy to achieve better noise reduction and higher compression with controlled degradation in data fidelity. A cost function is used in this work for efficient searching of full binary tree of WPD to obtain an accurate representation of a given signal. The data analysis, compression, and denoising are performed through selection of a suitable wavelet function, decomposition the signal up to the optimum level, determination of the best tree representation, calculate threshold at different levels, application of the threshold to the coefficients, and reconstruction of the signal. The proposed method is evaluated using a set of frequency disturbance recorder (FDR), phasor measurement unit (PMU), and load voltage data. Comparative results are presented with the wavelet decomposition (WD) and the built-in Matlab function ‘wpdencmp.’

Wavelet Packet Analysis for Automatic Seizure Detection and Latency Study in Scalp EEG

Wavelet Packet Analysis for Automatic Seizure Detection and Latency Study in Scalp EEG

Parkinson’s disease hand tremor detection system for mobile application

Parkinson’s disease currently affects millions of people worldwide and is steadily increasing. Many symptoms are associated with this disease, including rest tremor, bradykinesia, stiffness or rigidity of the extremities and postural instability. No cure is currently available for Parkinson’s disease patients; instead most medications are for treatment of symptoms. This treatment depends on the quantification of these symptoms such as hand tremor. This work proposes a new system for mobile phone applications. The system is based on measuring the acceleration from the Parkinson’s disease patient’s hand using a mobile cell phone accelerometer. Recordings from 21 Parkinson’s disease patients and 21 healthy subjects were used. These recordings were analysed using a two level wavelet packet analysis and features were extracted forming a feature vector of 12 elements. The features extracted from the 42 subjects were classified using a neural networks classifier. The results obtained showed an accuracy of 95% and a Kappa coefficient of 90%. These results indicate that a cell phone accelerometer can accurately detect and record rest tremor in Parkinson’s disease patients.

Automated drowsiness detection through wavelet packet analysis of a single EEG channel

Advances in materials engineering, electronic circuits, sensors, signal processing and classification techniques have allowed computational systems to interpret biological quantities, recognizing physiological conditions. The next scientific challenge is to turn those technologies portable, wearable or even implantable, above all, being energy efficient. A prospective application for the next generation of portable electroencephalogram (EEG) signal processing systems is hazard prevention in attention-demanding activities. EEG keeps closest connection to the preoptic area where sleep is originated. In this paper, a methodology for assessing alertness level based on a single EEG channel (Pz–Oz) is proposed, allowing the reduction of the required hardware and the computational time of the algorithms, besides being more portable than multi-channel based ones. Two new spectral power-based indices (i) γ/δ and (ii) (γ+β)/(δ+α) are computed from EEG rhythms through the normalized Haar discrete wavelet packet transform (WPT). The Haar WPT allows precisely resolving the brain rhythms into packets whilst demanding a relatively low computational cost. The effectiveness of the proposed indices in drowsiness detection is evaluated by comparison with five indices originally proposed for multi-channel processing. Statistical Wilcoxon signed rank test is applied to evaluate the performance of the entire set of indices, evidencing the significant changes in the alert-drowsy transitions of 20 subjects of a public database. The proposed indices (ii) and (i) presented the most and second more significant p-Values (p < 0.001 and p = 0.001), respectively.

Seismic random noise attenuation using artificial neural network and wavelet packet analysis

In this paper, we present a method for attenuating background random noise and enhancing resolution of seismic data, which takes advantage of semi-automatic training of feed forward back propagation (FFBP) artificial neural network (ANN) in a multiscale domain obtained from wavelet packet analysis (WPA). The images of approximations and details of the input seismic sections are calculated and utilized to train neural network to model coherent events by an automatic algorithm. After the modeling of coherent events, the remainder data are assumed to be related to background random noise. The proposed method is applied on both synthetic and real seismic data. The results are compared with that of the adaptive Wiener filter (AWF) in synthetic shot gather and real common midpoint gather and also with that of band-pass filtering on real common offset gather. The comparison indicates substantially higher efficiency of the proposed method in attenuating random noise and enhancing seismic signals.

Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform

Steel jacket type offshore platforms are highly susceptible to damage because of the severe environmental condition, therefore ensuring its proper performance and detection of probable damage is very important and undeniable. In this paper, by studying on experimental results of a prototype scaled offshore platform subjected to dynamic loading for different damage conditions, efficiency of Rate of Signal Energy using Wavelet Packet Transform for damage detection of this kind of structures has been carried out. Experiments results of two support conditions including hinge based and pile supported cases have been used. Wavelet packet analysis was used to determine the location of damage for different damage scenarios. The results demonstrate that proposed method can predict location of damage accurately. It can be concluded that this method can be used for prediction of damages similar to damages considered in this paper by using a continuous measurements and monitoring of platform responses.

Research on biomechanical compatibility for the artificial anal sphincter based on rectal perception function reconstruction

To treat the anal incontinence, a new artificial anal sphincter is put forward. Aiming at the rectal perception function reconstruction and the mechanical compatibility for artificial anal sphincter, this paper regards the huge mobile contraction wave in the typical rectal pressure contraction waves as the main signal of defecation. Meanwhile, by extracting features of rectal pressure signals with the help of wavelet packet analysis, and by adopting the Davies Bouldin index (DB) optimal algorithm, rectal perception function reconstruction is realised through predicting the defecation by means of the SVM classifier. In order to avoid the rectal ischemia necrosis, a three-dimensional finite element model for the rectum and artificial anal sphincter is constructed, with the purpose of analysing the viscoelasticity of the rectum. Then, prony series is deduced by utilising the constitutive equation of rectum, and finite element analysis for artificial anal sphincter is conducted by simulating the contraction and relaxation of the sphincter to generate load. At last, the shift and the stress distribution for the rectum at the time of being oppressed are obtained. This simulative experiment result shows that the artificial anal sphincter can highly accurately predict the defecation and it features favorable biomechanical compatibility.

Recovery of the 6-year signal in length of day and its long-term decreasing trend

There is a significant 6-year oscillation signal (called 6-year signal in this paper) existing in the interannual variations of length of day (LOD). It is unclear to understand its nature variation features. This paper extracts quantitatively the 6-year signal, from 1962~2012, using normal Morlet wavelet (NMWT) method combining wavelet packet and Fourier analysis technique, for the first time, and we investigate it in both time and frequency domains. The results indicate that the amplitude of a 6-year signal shows a long-term decreasing trend and the total amplitude reduction is about 0.05 ms during the past 50 years. The ratio of above reduction to the mean amplitude of 0.124 ms reaches 40 %. For interpreting the phenomenon on the above long-term decreasing trend, this paper proposes two alternatives; however, there is still no firm conclusion and it is required to be further explored.

Wavelet analysis of cardiac optical mapping data

BackgroundOptical mapping technology is an important tool to study cardiac electrophysiology. Transmembrane fluorescence signals from voltage-dependent dyes need to be preprocessed before analysis to improve the signal-to-noise ratio. Fourier analysis, based on spectral properties of stationary signals, cannot directly provide information on the spectrum changes with respect to time. Fourier filtering has the disadvantage of causing degradation of abrupt waveform changes such as those in action potential signals. Wavelet analysis has the ability to offer simultaneous localization in time and frequency domains, suitable for the analysis and reconstruction of irregular, non-stationary signals like the fast action-potential upstroke, and better than conventional filters for denoising. MethodsWe applied discrete wavelet transformation for temporal processing of optical mapping signals and wavelet packet analysis approaches to process activation maps from simulated and experimental optical mapping data from canine right atrium. We compared the results obtained with the wavelet approach to a variety of other methods (Fast Fourier Transformation (FFT) with finite or infinite response filtering, and Gaussian filters).Results: Temporal wavelet analysis improved signal-to-noise ratio (SNR) better than FFT filtering for 5–10dB SNR, and caused less distortion of the action potential waveform over the full range of simulated noise (5–20dB). Spatial wavelet filtering produced more efficient denoising and/or more accurate conduction velocity estimates than Gaussian filtering. Propagation patterns were also best revealed by wavelet filtering.Conclusions: Wavelet analysis is a promising tool, facilitating accurate action potential characterization, activation map formation, and conduction velocity estimation.

Speech enhancement based on wavelet packet of an improved principal component analysis

In this paper, we propose a single-channel speech enhancement method, based on the combination of the wavelet packet transform and an improved version of the principal component analysis (PCA). Our method integrates ability of PCA to de-correlate the coefficients by extracting a linear relationship with what of wavelet packet analysis to derive feature vectors used for speech enhancement. This allows us to operate with a convenient shrinkage function on these new coefficients, removing the noise without degrading the speech. Then, the enhanced speech obtained by the inverse wavelet packet transform is decomposed into three subspaces: low rank, sparse, and the remainder noise components. Finally, we calculate the components as a segregation problem. The performance evaluation shows that our method provides a higher noise reduction and a lower signal distortion even in highly noisy conditions without introducing artifacts.

Reliability prediction method based on state space model for rolling element bearing

Reliability analysis based on equipment’s performance degradation characteristics is one of the significant research areas in reliability research. Nowadays, many researches are carried on multi-sample analysis. But it is limited for a single equipment reliability prediction. Therefore, the method of reliability prediction based on state space model (SSM) is proposed in this research. Feature energy of the monitored signals is extracted with the wavelet packet analysis and the associated frequency band energy with online monitored data. Then, degradation feature is improved by moving average filtering processing taken as input pair model parameter of SSM to be estimated. In the end, state space predicting model of degradation index is established. The probability density distribution of the degradation index is predicted, and the degree of reliability is calculated. A real testing example of bearing is used to demonstrate the rationality and effectiveness of this method. It is a useful method for single sample reliability prediction.

Feature Extraction Technology for Rolling Bearings Based on Local Tangent Space Alignment

Health assessment and fault diagnosis for rolling bearings mostly adopt traditional methods, such as time-frequency, spectral, and wavelet packet analyses, to extract the feature vector. These methods are suitable for processing data with a linear structure. However, for the non-linear and non-stationary signal, the result of these methods is not ideal. Thus, this study proposes a suitable method to extract the feature vector in nonlinear signals. Local tangent space alignment of a manifold algorithm is employed to extract the feature vector from the rolling bearings. Results verify the advantage of the manifold algorithm for non-linear and non-stationary signals.

Improved Thermography Technique for Identifying Structural Elements under Ambient Conditions

Infrared thermography is an interesting non-contact evaluation technique which relies on surface thermal gradient. It is used to identify dissimilarity in geometry or material property. The application of this method in civil engineering is limited, as concrete components are heavy and require large amount of heat to reveal differential thermal gradients. Hence, the present work focuses on assessing the applicability of the infrared thermography technique for civil engineering structures under ambient conditions using solar heat. For the study, the exterior frames in real structures have been chosen, wherein the reinforced concrete and masonry members are not explicitly visible to the eye. The initial studies showed that the thermograms revealed different temperature gradients corresponding to the main beams and masonry walls, thus aiding in their identification. However, when the method was extended to multi-storey framed structures, the thinner elements like lintel beams could not be identified. The application of improved thermography technique using wavelet packet analysis to two test structures showed that lintel beams could also be identified. Also, the possibility to evaluate the geometric dimensions of structural members and floor heights, using the thermogram and a reference dimension, has been studied on two test structures. The geometric dimensions could be evaluated with more than 90% accuracy.

Event Extraction of Acoustic Emission using Wavelet Packets and Correlation Analysis

Event Extraction of Acoustic Emission using Wavelet Packets and Correlation Analysis

Study on Acoustic Emission Signal Frequency Analysis for Diamond Growth Process

The growth process of diamond single crystals under HPHT was tested with acoustic emission (AE) technology, and the AE signals were analyzed with fast Fourier transform and wavelet packet analysis. The results show that there are many new frequency peaks in the frequency range that is higher than 80 kHz, and comparative analysis shows that these new frequency peaks are caused from the growth process of diamond crystals. The value of the signal energy and peak in different frequency bands are compared along with the diamond growth process. And it has reflected the dynamic changes in the time domain of the acoustic emission signals stimulated by the different acoustic emission sources. It also shows that the frequency of acoustic emission signals could be used as the effective means to distinguish the different acoustic emission sources in the diamond growth process.

Rough-fuzzy clustering and multiresolution image analysis for text-graphics segmentation

This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text or graphics regions of a given document are considered to have different textural properties. The M-band wavelet packet analysis and rough-fuzzy-possibilistic c-means are used for text-graphics segmentation problem. The M-band wavelet packet is used to extract the scale-space features, which offers a huge range of possibilities of scale-space features for document image and is able to zoom it onto narrow band high frequency components. A scale-space feature vector is thus derived, taken at different scales for each pixel in an image. However, the decomposition scheme employing M-band wavelet packet leads to a large number of redundant features. In this regard, an unsupervised feature selection method is introduced to select a set of relevant and non-redundant features for text-graphics segmentation problem. Finally, the rough-fuzzy-possibilistic c-means algorithm is used to address the uncertainty problem of document segmentation. The whole approach is invariant under the font size, line orientation, and script of the text. The performance of the proposed technique, along with a comparison with related approaches, is demonstrated on a set of real life document images.

Acoustic emission detection for mass fractions of materials based on wavelet packet technology

Materials are often damaged during the process of detecting mass fractions by traditional methods. Acoustic emission (AE) technology combined with wavelet packet analysis is used to evaluate the mass fractions of microcrystalline graphite/polyvinyl alcohol (PVA) composites in this study. Attenuation characteristics of AE signals across the composites with different mass fractions are investigated. The AE signals are decomposed by wavelet packet technology to obtain the relationships between the energy and amplitude attenuation coefficients of feature wavelet packets and mass fractions as well. Furthermore, the relationship is validated by a sample. The larger proportion of microcrystalline graphite will correspond to the higher attenuation of energy and amplitude. The attenuation characteristics of feature wavelet packets with the frequency range from 125kHz to 171.85kHz are more suitable for the detection of mass fractions than those of the original AE signals. The error of the mass fraction of microcrystalline graphite calculated by the feature wavelet packet (1.8%) is lower than that of the original signal (3.9%). Therefore, AE detection base on wavelet packet analysis is an ideal NDT method for evaluate mass fractions of composite materials.