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Showing papers on "Wavelet published in 2012"


Journal ArticleDOI
TL;DR: This paper constructs translation-invariant operators on L 2 .R d /, which are Lipschitz-continuous to the action of diffeomorphisms, and extendsScattering operators are extended on L2 .G/, where G is a compact Lie group, and are invariant under theaction of G.
Abstract: This paper constructs translation-invariant operators on L 2 .R d /, which are Lipschitz-continuous to the action of diffeomorphisms. A scattering propagator is a path-ordered product of nonlinear and noncommuting operators, each of which computes the modulus of a wavelet transform. A local integration defines a windowed scattering transform, which is proved to be Lipschitz-continuous to the action of C 2 diffeomorphisms. As the window size increases, it converges to a wavelet scattering transform that is translation invariant. Scattering coefficients also provide representations of stationary processes. Expected values depend upon high-order moments and can discriminate processes having the same power spectrum. Scattering operators are extended on L 2 .G/, where G is a compact Lie group, and are invariant under the action of G. Combining a scattering on L 2 .R d / and on L 2 .SO.d// defines a translation- and rotation-invariant scattering on L 2 .R d /. © 2012 Wiley Periodicals, Inc.

941 citations


Posted Content
TL;DR: A wavelet scattering network as discussed by the authors computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification, cascading wavelet transform convolutions with nonlinear modulus and averaging operators.
Abstract: A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It cascades wavelet transform convolutions with non-linear modulus and averaging operators. The first network layer outputs SIFT-type descriptors whereas the next layers provide complementary invariant information which improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State of the art classification results are obtained for handwritten digits and texture discrimination, using a Gaussian kernel SVM and a generative PCA classifier.

630 citations


Journal ArticleDOI
TL;DR: A novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage, which compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio and of perceived image quality.
Abstract: We propose a novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the block-matching 3-D algorithm, recently proposed for additive white Gaussian noise denoising, but modifies its major processing steps in order to take into account the peculiarities of SAR images. A probabilistic similarity measure is used for the block-matching step, while the wavelet shrinkage is developed using an additive signal-dependent noise model and looking for the optimum local linear minimum-mean-square-error estimator in the wavelet domain. The proposed technique compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio (on simulated speckled images) and of perceived image quality.

601 citations


Journal ArticleDOI
TL;DR: An unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm that exhibited lower error than its preexistences.
Abstract: This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

508 citations


Journal ArticleDOI
TL;DR: A new wavelet-based method for removing motion artifacts from fNIRS signals based on a gaussian distribution and modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact is proposed.
Abstract: Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a Gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than −16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

391 citations


Journal ArticleDOI
TL;DR: This work uses wavelet coherence to uncover interesting dynamics of correlations between energy commodities in the time-frequency space and proposes a new, model-free way of estimating time-varying correlations.

372 citations


Journal ArticleDOI
TL;DR: The proposed method 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.

362 citations


Journal ArticleDOI
TL;DR: This paper is designed to establish connections between these two major image restoration approaches: variational methods and wavelet frame based methods to provide new interpretations and understanding of both approaches, and hence, lead to new applications for both approaches.
Abstract: From the beginning of science, visual observations have been playing important roles. Advances in computer technology have made it possible to apply some of the most sophisticated developments in mathematics and the sciences to the design and implementation of fast algorithms running on a large number of processors to process image data. As a result, image processing and analysis techniques are now applied to virtually all natural sciences and technical disciplines ranging from computer sciences and electronic engineering to biology and medical sciences; and digital images have come into everyone’s life. Image restoration, including image denoising, deblurring, inpainting, computed tomography, etc., is one of the most important areas in image processing and analysis. Its major purpose is to enhance the quality of a given image that is corrupted in various ways during the process of imaging, acquisition and communication, and enables us to see crucial but subtle objects reside in the image. Therefore, image restoration is an important step to take towards the accurate interpretations of the physical world and making the optimal decisions. Mathematics has been playing an important role in image and signal processing from the very beginning; for example, Fourier analysis is one of the main tools in signal and image analysis, processing, and restoration. In fact, mathematics has been one of the driving forces of the modern development of image analysis, processing and restorations. At the same time, the interesting and challenging problems in imaging science also gave birth to new mathematical theories, techniques and methods. The variational methods (e.g. total variation based methods) and wavelets and wavelet frame based methods developed in the last few decades for image and signal processing are two successful recent examples among many. This paper is designed to establish connections between these two major image restoration approaches: variational methods and wavelet frame based methods. Such connections provide new interpretations and understanding of both approaches, and hence, lead to new applications for both approaches. We start with an introduction of both the variational and wavelet frame based methods. The basic linear image restoration model used for variational methods is

359 citations


Journal ArticleDOI
TL;DR: The generalized Morse wavelets are shown to constitute a superfamily that essentially encompasses all other commonly used analytic wavelets, subsuming eight apparently distinct types of analysis filters into a single common form.
Abstract: The generalized Morse wavelets are shown to constitute a superfamily that essentially encompasses all other commonly used analytic wavelets, subsuming eight apparently distinct types of analysis filters into a single common form. This superfamily of analytic wavelets provides a framework for systematically investigating wavelet suitability for various applications. In addition to a parameter controlling the time-domain duration or Fourier-domain bandwidth, the wavelet shape with fixed bandwidth may be modified by varying a second parameter, called γ. For integer values of γ, the most symmetric, most nearly Gaussian, and generally most time-frequency concentrated member of the superfamily is found to occur for γ = 3. These wavelets, known as “Airy wavelets,” capture the essential idea of popular Morlet wavelet, while avoiding its deficiencies. They may be recommended as an ideal starting point for general purpose use.

288 citations


Journal ArticleDOI
TL;DR: In this article, the authors compare the utility of a variety of motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts, including spline interpolation, wavelet analysis, and Kalman filtering.
Abstract: Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.

283 citations


Journal Article
TL;DR: The routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) are recommended to minimize the impact of motion artifacts on functional NIRS data.
Abstract: Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.

Journal ArticleDOI
TL;DR: Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity.
Abstract: In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR) images is developed based on wavelet transform as a feature extraction tool and support vector machine (SVM) as a classifier. SAR image segmentation is an important step in texture recognition of SAR images. SAR images cannot be segmented successfully by using traditional methods because of the existence of speckle noise in SAR images. The algorithm, proposed in this paper, extracts the texture feature by using wavelet transform; then, it forms a feature vector composed of kurtosis value of wavelet energy feature of SAR image. In the next step, segmentation of different textures is applied by using feature vector and level set function. At last, an SVM classifier is designed and trained by using normalized feature vectors of each region texture. The testing sets of SAR images are segmented by this trained SVM. Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity.

Journal ArticleDOI
TL;DR: Testing on several image databases demonstrates that, despite its simplicity, FISH is competitive with the currently best-performing techniques both for sharpness estimation and for no-reference image quality assessment.
Abstract: In this letter, we present a simple, yet effective wavelet-based algorithm for estimating both global and local image sharpness (FISH, Fast Image Sharpness). FISH operates by first decomposing the input image via a three-level separable discrete wavelet transform (DWT). Next, the log-energies of the DWT subbands are computed. Finally, a scalar index corresponding to the image's overall sharpness is computed via a weighted average of these log-energies. Testing on several image databases demonstrates that, despite its simplicity, FISH is competitive with the currently best-performing techniques both for sharpness estimation and for no-reference image quality assessment.

Journal ArticleDOI
31 Jul 2012
TL;DR: A novel wavelet-based automatic seizure detection method with high sensitivity and specificity that could achieve a sensitivity of 94.46% and a specificity of 95.26% with a false detection rate of 0.58/h for seizure detection in long-term iEEG.
Abstract: Automatic seizure detection is of great significance for epilepsy long-term monitoring, diagnosis, and rehabilitation, and it is the key to closed-loop brain stimulation. This paper presents a novel wavelet-based automatic seizure detection method with high sensitivity. The proposed method first conducts wavelet decomposition of multi-channel intracranial EEG (iEEG) with five scales, and selects three frequency bands of them for subsequent processing. Effective features are extracted, such as relative energy, relative amplitude, coefficient of variation and fluctuation index at the selected scales, and then these features are sent into the support vector machine for training and classification. Afterwards a postprocessing is applied on the raw classification results to obtain more accurate and stable results. Postprocessing includes smoothing, multi-channel decision fusion and collar technique. Its performance is evaluated on a large dataset of 509 h from 21 epileptic patients. Experiments show that the proposed method could achieve a sensitivity of 94.46% and a specificity of 95.26% with a false detection rate of 0.58/h for seizure detection in long-term iEEG.

Journal ArticleDOI
TL;DR: A new wavelet shrinkage approach allows the distributed vibration measurement of 20-Hz and 8-kHz events to be detected over 1-km sensing length with a 5-ns optical pulse, which is equivalent to 50-cm spatial resolution using the single-mode sensing fiber.
Abstract: This letter proposed and demonstrated a wavelet technique to reduce the time domain noise to get submeter spatial resolution in the distributed vibration sensor based on phase optical time domain reflectometry. A new wavelet shrinkage approach allows the distributed vibration measurement of 20-Hz and 8-kHz events to be detected over 1-km sensing length with a 5-ns optical pulse, which is equivalent to 50-cm spatial resolution using the single-mode sensing fiber.

Proceedings ArticleDOI
18 Apr 2012
TL;DR: In this study, the most commonly used methods including GIHS, GIHSF, PCA and Wavelet are analyzed using image quality metrics such as SSIM, ERGAS and SAM to find the best method for obtaining the fused image having the least spectral distortions according to obtained results.
Abstract: In literature, several methods are available to combine both low spatial multispectral and low spectral panchromatic resolution images to obtain a high resolution multispectral image. One of the most common problems encountered in these methods is spectral distortions introduced during the merging process. At the same time, the spectral quality of the image is the most important factor affecting the accuracy of the results in many applications such as object recognition, object extraction, image analysis. In this study, the most commonly used methods including GIHS, GIHSF, PCA and Wavelet are analyzed using image quality metrics such as SSIM, ERGAS and SAM. At the same time, Wavelet is the best method for obtaining the fused image having the least spectral distortions according to obtained results. At the same time, image quality of GIHS, GIHSF and PCA methods are close to each other, but spatial qualities of the fused image using the wavelet method are less than others.

Journal ArticleDOI
Xiaobo Qu1, Di Guo1, Bende Ning1, Yingkun Hou, Yulan Lin1, Shuhui Cai1, Zhong Chen1 
TL;DR: Simulation results on phantom and in vivo data indicate that the proposed patch-based directional wavelets method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise.

Journal ArticleDOI
TL;DR: A Hybrid model HTW-MPNN is implemented to achieve prominent prediction of crude oil price by combining the dynamic properties of multilayer back propagation neural network and the recent Harr A trous wavelet decomposition, providing robust simulations on both in sample and out of sample basis.

Journal ArticleDOI
TL;DR: This paper examines the use of wavelet detail coefficients for the accurate detection of different QRS morphologies in ECG based on the power spectrum of QRS complexes in different energy levels since it differs from normal beats to abnormal ones.

Journal ArticleDOI
TL;DR: In this paper, a new trend detection method for hydrological studies is explored, which involves the use of wavelet transforms (WTs) in order to separate the rapidly and slowly changing events contained in a time series.

Journal ArticleDOI
01 Jan 2012
TL;DR: This paper proposes a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies, achieving an accuracy of around 93% using tenfold cross validations.
Abstract: Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naive Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

Journal ArticleDOI
TL;DR: The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.
Abstract: Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal from scalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi's entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the “wavelet enhanced” ICA method recently proposed by other authors. Simulations illustrate that AWICA compares favorably to the other technique. The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.

Journal ArticleDOI
TL;DR: Comparison study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system demonstrates the advantages of S -transform over WT in detection of Islanding and different disturbances under noise-free as well as noisy scenarios.
Abstract: In this paper, comparative study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system is presented. The hybrid system consists of DG resources like photovoltaic, fuel cell, and wind energy systems connected to grid. The negative sequence component of the voltage signal is used in islanding detection of these resources from the grid. Voltage signal extracted directly at the point of common coupling is considered for detection of PQ disturbances. Further, the effect of variation of grid impedances on islanding and PQ disturbances and effect of islanding on the coherency between the energy resources is also presented in this paper. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation of ST contour and WT signal is also reported in order to validate the graphical results. The results demonstrate the advantages of S -transform over WT in detection of islanding and different disturbances under noise-free as well as noisy scenarios.

Journal ArticleDOI
TL;DR: Results obtained using continuous and discrete wavelet transforms as applied to problems in neurodynamics are reviewed, with the emphasis on the potential of wavelet analysis for decoding signal information from neural systems and networks.
Abstract: Results obtained using continuous and discrete wavelet transforms as applied to problems in neurodynamics are reviewed, with the emphasis on the potential of wavelet analysis for decoding signal information from neural systems and networks. The following areas of application are considered: (1) the microscopic dynamics of single cells and intracellular processes, (2) sensory data processing, (3) the group dynamics of neuronal ensembles, and (4) the macrodynamics of rhythmical brain activity (using multichannel EEG recordings). The detection and classification of various oscillatory patterns of brain electrical activity and the development of continuous wavelet-based brain activity monitoring systems are also discussed as possibilities.

Journal ArticleDOI
01 Aug 2012
TL;DR: A vibration based condition monitoring system for monoblock centrifugal pumps and the use of Naive Bayes algorithm and Bayes net algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump is presented.
Abstract: In most of the industries related to mechanical engineering, the usage of pumps is high. Hence, the system which takes care of the continuous running of the pump becomes essential. In this paper, a vibration based condition monitoring system is presented for monoblock centrifugal pumps as it plays relatively critical role in most of the industries. This approach has mainly three steps namely feature extraction, classification and comparison of classification. In spite of availability of different efficient algorithms for fault detection, the wavelet analysis for feature extraction and Naive Bayes algorithm and Bayes net algorithm for classification is taken and compared. This paper presents the use of Naive Bayes algorithm and Bayes net algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different discrete wavelet families were calculated and compared to find the best wavelet for the fault diagnosis of the centrifugal pump.

Journal ArticleDOI
TL;DR: A generalized synchrosqueezing transform (GST) approach to deal with the diffusions in both time and frequency dimensions is proposed for signal TFR enhancement and it is shown that the wavelet diffusion only occurs at frequency dimension.

Journal ArticleDOI
TL;DR: It is demonstrated that a straightforward NLM-based denoising scheme provides signal-to-noise ratio improvements very similar to state of the art wavelet-based methods, while giving greater reduction in metrics measuring distortion of the denoised waveform.
Abstract: Patch-based methods have attracted significant attention in recent years within the field of image processing for a variety of problems including denoising, inpainting, and super-resolution interpolation. Despite their prevalence for processing 2-D signals, they have received little attention in the 1-D signal processing literature. In this letter, we explore application of one such method, the nonlocal means (NLM) approach, to the denoising of biomedical signals. Using ECG as an example, we demonstrate that a straightforward NLM-based denoising scheme provides signal-to-noise ratio improvements very similar to state of the art wavelet-based methods, while giving $\sim$ 3 $\times$ or greater reduction in metrics measuring distortion of the denoised waveform.

Journal ArticleDOI
TL;DR: Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.
Abstract: In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.

Book
01 Oct 2012
TL;DR: In this article, the wavelet-based techniques that have applied to turbulence problems are explained and the main results obtained are summarized, and the theory and open questions encountered in turbulence are presented.
Abstract: We have used wavelet transform techniques to analyze, model, and compute turbulent flows. The theory and open questions encountered in turbulence are presented. The wavelet-based techniques that we have applied to turbulence problems are explained and the main results obtained are summarized.

Journal ArticleDOI
TL;DR: A novel automatic technique based on data mining for epileptic EEG classification into the normal, interictal, and ictal activities, which is capable of classifying EEG segments with clinically acceptable accuracy using less number of features that can be extracted with less computational cost is presented.
Abstract: Highlights? We present a novel automatic technique for epileptic EEG classification into the normal, interictal, and ictal activities. ? We use eigenvalues extracted from wavelet coefficients as features. ? Several supervised learning based classifiers were trained using selected features. ? We demonstrate that our proposed technique can yield 99% classification accuracy. Electroencephalogram (EEG) signals are used to detect and study the characteristics of epileptic activities. Owing to the non-linear and dynamic nature of EEG signals, visual inspection and interpretation of these signals are tedious, time-consuming, error-prone, and subjected to inter-observer variabilities. Therefore, several Computer Aided Diagnostic (CAD) based studies have adopted non-linear techniques to study the normal, interictal, and ictal activities in EEGs. In this paper, we present a novel automatic technique based on data mining for epileptic activity classification. In order to compare our study with the results of relative studies in the literature, we used the widely used benchmark dataset from Bonn University for evaluation of our proposed technique. Hundred samples each in normal, interictal, and ictal categories were used. We decomposed these segments into wavelet coefficients using Wavelet Packet Decomposition (WPD), and extracted eigenvalues from the resultant wavelet coefficients using Principal Component Analysis (PCA). Significant eigenvalues, selected using the ANOVA test, were used to train and test several supervised classifiers using the 10-fold stratified cross validation technique. We obtained 99% classification accuracy using the Gaussian Mixture Model (GMM) classifier. The proposed technique is capable of classifying EEG segments with clinically acceptable accuracy using less number of features that can be extracted with less computational cost. The technique can be written as a software application that can be easily deployed at a low cost and used with almost no expert training. We foresee that this software can, in the future, evolve into an efficient adjunct tool that cannot only classify epileptic activities in EEG signals but also automatically monitor the onset of seizures and thereby aid the doctors in providing better and timely care for the patients suffering from epilepsy.