Anti-symmetrical Biorthogonal Wavelet Research Articles

Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals

Sleep is a fundamental human physiological activity required for adequate working of the human body. Sleep disorders such as sleep movement disorders, nocturnal front lobe epilepsy, insomnia, and narcolepsy are caused due to low sleep quality. Insomnia is one such sleep disorder where a person has difficulty in getting quality sleep. There is no definitive test to identify insomnia; hence it is essential to develop an automated system to identify it accurately. A few automated methods have been proposed to identify insomnia using either polysomnogram (PSG) or electroencephalogram (EEG) signals. To the best of our knowledge, we are the first to automatically detect insomnia using only electrocardiogram (ECG) signals without combining them with any other physiological signals.In the proposed study, an optimal antisymmetric biorthogonal wavelet filter bank (ABWFB) has been used, which is designed to minimize the joint duration-bandwidth localization (JDBL) of the underlying filters. The L1-norm feature is computed from the various wavelet sub-bands coefficients of ECG signals. The L1 norm features are fed to various supervised machine learning classifiers for the automated detection of insomnia. In this work, ECG recordings of seven insomnia patients and six normal subjects are used from the publicly available cyclic alternating pattern (CAP) sleep database.We created ten different subsets of ECG signals based on annotations of sleep-stages, namely wake (W), S1, S2, S3, S4, rapid eye moment (REM), light sleep stage (LSS), slow-wave sleep (SWS), non-rapid eye movement (NREM) and W + S1+S2+S3+S4+REM for the automated identification of insomnia. Our proposed ECG-based system obtained the highest classification accuracy of 97.87%, F1-score of 97.39%, and Cohen's kappa value of 0.9559 for K-nearest neighbour (KNN) with the ten-fold cross-validation strategy using ECG signals corresponding to the REM sleep stage. The support vector machine (SVM) yielded the highest value of 0.99 for area under the curve with the ten fold cross-validation corresponding to REM sleep stage.

A neural network ensemble method for effective crack segmentation using fully convolutional networks and multi-scale structured forests

Crack image segmentation has recently become a major research topic in nondestructive inspection. However, the image segmentation methods are not robust to variations such as illumination, weather, noise and the segmentation accuracy which cannot meet the requirements of practical applications. Therefore, a neural network ensemble method is proposed for effective crack segmentation in this paper, which consists of fully convolution networks (FCN) and multi-scale structured forests for edge detection (SFD). In order to improve the accuracy of crack segmentation and reduce the error mark under complex background, a new network model based on FCN model is proposed to address the problems that lose local information and the capacity of partial refinement, which are frequently encountered in FCN model in the crack segmentation. In addition, SFD is combined with the half-reconstruction method of anti-symmetrical bi-orthogonal wavelet to overcome the limitation of crack edge detection. Finally, the result of the two maps is merged after resizing to the original image dimensions. Qualitative and quantitative evaluations of the proposed methods are performed, showing that they can obtain better results than certain existing methods for crack segmentation.

A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank

The extreme deposition of plaque in the inner walls of arteries causes coronary artery disease (CAD). It can be detected by the morphological changes in the electrocardiogram (ECG) signals. The manual analysis of ECG signals is inefficient, as it is laborious and vulnerable to errors. CAD diagnosis tests also demand physical work from patients, which may not be fulfilled as readily by the elderly and physically challenged patients. The use of automated coronary artery disease diagnosis system can help to overcome the aforementioned problems. In this study, ECG segments of different durations (2s and 5s) are employed for the analysis of CAD. We propose the use of a recently developed optimally time-frequency concentrated (OTFC) even-length biorthogonal wavelet filter bank (BWFB) for automatically identifying CAD. The fuzzy entropy (FE) and log-energy (LogE) were extracted from the OTFC decomposed coefficients. Using the Gaussian support vector machine (GSVM) classifier, an average classification accuracy of 99.53% is achieved with 10-fold cross-validation (CV). The average sensitivity and specificity obtained are 98.64% & 99.70%, respectively with the Matthews correlation coefficient(MCC) of 0.983. The classification performance of the proposed model has surpassed most of the state-of-art models. The method presented in this paper can be of great help to clinicians and cardiologists to validate their diagnosis. Our developed model is economical, robust and accurate in diagnosing the CAD.

Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters

We present a design of a new class of compactly supported antisymmetric biorthogonal wavelet filter banks which have the analysis as well as the synthesis filters of even-length. Here, the analysis and the synthesis filters are designed to have minimum joint duration-bandwidth localization (JDBL). The design of filters has been formulated as a direct time-domain linearly constrained eigenvalue problem that does not involve any parametrization and iterations. The optimal analysis and synthesis filters have been obtained as the eigenvectors of the positive definite matrices. The closed form analytic expression for the objective function has been presented. The perfect reconstruction and regularity conditions have been incorporated in the design by employing time-domain matrix characterization. The method can control duration and bandwidth localizations of the analysis and synthesis filters, independently. A few design examples have been presented and compared with previous works. The performance of the optimal filter banks designed by employing the proposed method has been evaluated in image coding and signal denoising applications.

Image Edge Detection Based on Anti-Symmetrical Biorthogonal Wavelet Filter Banks with the Same Even Length

With the development of the theory of wavelet transform, biorthogonal wavelet filter banks with linear phase and compact support characteristics are widely used in signal and image processing. We study the multiscale edge detection using anti-symmetrical biorthogonal wavelet filter banks with the same even length based on the analysis of the properties of this wavelet filter banks. The steps of multiscale edge detection based on anti-symmetrical biorthogonal wavelet are introduced in detail. Experimental results on test images show that compared with the anti-symmetrical biorthogonal wavelet, the proposed anti-symmetrical biorthogonal wavelet filter banks with the same even length have better performance in terms of image edge detection. Therefore, the anti-symmetrical biorthogonal wavelet filter banks with the same even length are more suitable for image edge detection.

Adaptive Threshold Edge Detection Algorithm Based on APDCSF and Anti-symmetrical Biorthogonal Wavelet Transform

This paper proposes an edge detection algorithm based on adaptive threshold which is obtained by histogram matching edge gradient value. The implementation of this algorithm is made up of three procedures: all phase discrete cosine sequency filtering, wavelet decomposition and adaptive threshold processing. The image is decomposed into low frequency and high frequency subbands by using all phase discrete cosine sequency filter (APDCSF). Experimental results on test images reveal that compared with the edge detection algorithm based on artificial threshold, the proposed one can create dynamic threshold adaptively and show better edge detection performance especially in terms of edge positioning accuracy.

Image Enhancement Based on Anti-symmetrical Biorthogonal Wavelet Recontruction

In this paper, image multi-scale edge detection based on anti-symmetrical biorthogonal wavelet reconstruction is given detailedly in theory, namely convolution operation property of the anti-symmetrical biorthogonal wavelet trans- form, is deduced and its differential operator function is analyzed. Then, an algorithm of wavelet decomposition in which multi-scale edge can be detected is put forward. Based on the above, an novel approach of image enhancement based on anti-symmetrical biorthogonal wavelet reconstruction is presented. Firstly, multi-scale decomposition of wavelet of image is done. Then, module value and phasic angle image are achieved through half-reconstruction, by which edge images of different scales are extracted. And pixels of half-reconstruction image corresponding to edge image are enhanced. Finally, reconstruction is done and image is enhanced. In the approach presented, image edges are enhanced in the process of reconstruction of wavelet tower data. This method gives a new idea for image denoising and enhancement. Experimental results show the validity of the approach.

Textile Surface Inspection by Using Translation Invariant Wavelet Shrinkage

A visual inspection method of textile surfaces using the translation invariant Wavelet Shrinkage is presented. The Wavelet transform, while it can be computed efficiently by the Mallat algorithm, has the translation variance problem. To deal with this problem, we use RI-Spline wavelets which are pseudo Complex wavelets consist of a pair of a symmetric bi-orthogonal spline wavelet and an anti-symmetric bi-orthogonal spline wavelet, for textile surface inspection. In our approach, we remove the regular information which consists of the textile textures and the shading effects caused by uneven lighting from the textile surfaces to be inspected, using the translation invariant Wavelet Shrinkage realized using 2D RI-Spline wavelets. The experimental results show that our inspection method is effective for detecting tiny defects as well as global defects such as dyeing unevenness.