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Discrete wavelet transform

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https://www.readbyqxmd.com/read/28593391/wavelet-decomposition-analysis-in-the-two-flash-multifocal-erg-in-early-glaucoma-a-comparison-to-ganglion-cell-analysis-and-visual-field
#1
Livia M Brandao, Matthias Monhart, Andreas Schötzau, Anna A Ledolter, Anja M Palmowski-Wolfe
PURPOSE: To further improve analysis of the two-flash multifocal electroretinogram (2F-mfERG) in glaucoma in regard to structure-function analysis, using discrete wavelet transform (DWT) analysis. METHODS: Sixty subjects [35 controls and 25 primary open-angle glaucoma (POAG)] underwent 2F-mfERG. Responses were analyzed with the DWT. The DWT level that could best separate POAG from controls was compared to the root-mean-square (RMS) calculations previously used in the analysis of the 2F-mfERG...
June 7, 2017: Documenta Ophthalmologica. Advances in Ophthalmology
https://www.readbyqxmd.com/read/28574347/anisotropic-discrete-dual-tree-wavelet-transform-for-improved-classification-of-trabecular-bone
#2
Hind Oulhaj, Mohammed Rziza, Aouatif Amine, Hechmi Toumi, Eric Lespessailles, Mohammed El Hassouni, Rachid Jennane
This paper deals with a new Anisotropic Discrete Dual-Tree Wavelet Transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional Discrete Dual-Tree Wavelet Transform (DDTWT) by using the anisotropic basis functions associated with the Hyperbolic Wavelet Transform (HWT) instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The Generalized Gaussian Distribution (GGD) is used to model the distribution of the sub-band coefficients...
May 26, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28558318/a-new-near-lossless-eeg-compression-method-using-ann-based-reconstruction-technique
#3
Behzad Hejrati, Abdolhossein Fathi, Fardin Abdali-Mohammadi
Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually cannot efficiently extract signal redundancy especially for non-stationary signals such as electroencephalogram (EEG). In this paper, we first propose learning-based adaptive transform using combination of DCT and artificial neural network (ANN) reconstruction technique...
May 24, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28546862/block-sparsity-based-joint-compressed-sensing-recovery-of-multi-channel-ecg-signals
#4
Anurag Singh, Samarendra Dandapat
In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance...
April 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529760/denoising-techniques-in-adaptive-multi-resolution-domains-with-applications-to-biomedical-images
#5
Salim Lahmiri
Variational mode decomposition (VMD) is a new adaptive multi-resolution technique suitable for signal denoising purpose. The main focus of this work has been to study the feasibility of several image denoising techniques in empirical mode decomposition (EMD) and VMD domains. A comparative study is made using 11 techniques widely used in the literature, including Wiener filter, first-order local statistics, fourth partial differential equation, nonlinear complex diffusion process, linear complex diffusion process (LCDP), probabilistic non-local means, non-local Euclidean medians, non-local means, non-local patch regression, discrete wavelet transform and wavelet packet transform...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529759/high-frequency-based-features-for-low-and-high-retina-haemorrhage-classification
#6
Salim Lahmiri
Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD)...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28499122/emd-dwt-based-transform-domain-feature-reduction-approach-for-quantitative-multi-class-classification-of-breast-lesions
#7
Sharmin R Ara, Syed Khairul Bashar, Farzana Alam, Md Kamrul Hasan
Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i...
September 2017: Ultrasonics
https://www.readbyqxmd.com/read/28489019/heart-sound-classification-from-unsegmented-phonocardiograms
#8
Philip Langley, Alan Murray
Objective Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. Approach PCG segments of 5 second duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 second segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation...
May 10, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28487724/fast-compressed-sensing-mri-based-on-complex-double-density-dual-tree-discrete-wavelet-transform
#9
Shanshan Chen, Bensheng Qiu, Feng Zhao, Chao Li, Hongwei Du
Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts...
2017: International Journal of Biomedical Imaging
https://www.readbyqxmd.com/read/28484720/eeg-based-computer-aided-diagnosis-of-autism-spectrum-disorder-using-wavelet-entropy-and-ann
#10
Ridha Djemal, Khalil AlSharabi, Sutrisno Ibrahim, Abdullah Alsuwailem
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28463698/forecasting-of-groundwater-level-fluctuations-using-ensemble-hybrid-multi-wavelet-neural-network-based-models
#11
Rahim Barzegar, Elham Fijani, Asghar Asghari Moghaddam, Evangelos Tziritis
Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing)...
December 1, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28406919/what-are-the-assets-and-weaknesses-of-hfo-detectors-a-benchmark-framework-based-on-realistic-simulations
#12
Nicolas Roehri, Francesca Pizzo, Fabrice Bartolomei, Fabrice Wendling, Christian-George Bénar
High-frequency oscillations (HFO) have been suggested as biomarkers of epileptic tissues. While visual marking of these short and small oscillations is tedious and time-consuming, automatic HFO detectors have not yet met a large consensus. Even though detectors have been shown to perform well when validated against visual marking, the large number of false detections due to their lack of robustness hinder their clinical application. In this study, we developed a validation framework based on realistic and controlled simulations to quantify precisely the assets and weaknesses of current detectors...
2017: PloS One
https://www.readbyqxmd.com/read/28406916/applications-of-fractional-lower-order-s-transform-time-frequency-filtering-algorithm-to-machine-fault-diagnosis
#13
Junbo Long, Haibin Wang, Daifeng Zha, Peng Li, Huicheng Xie, Lili Mao
Stockwell transform(ST) time-frequency representation(ST-TFR) is a time frequency analysis method which combines short time Fourier transform with wavelet transform, and ST time frequency filtering(ST-TFF) method which takes advantage of time-frequency localized spectra can separate the signals from Gaussian noise. The ST-TFR and ST-TFF methods are used to analyze the fault signals, which is reasonable and effective in general Gaussian noise cases. However, it is proved that the mechanical bearing fault signal belongs to Alpha(α) stable distribution process(1 < α < 2) in this paper, even the noise also is α stable distribution in some special cases...
2017: PloS One
https://www.readbyqxmd.com/read/28381053/lithographic-source-optimization-based-on-adaptive-projection-compressive-sensing
#14
Xu Ma, Dongxiang Shi, Zhiqiang Wang, Yanqiu Li, Gonzalo R Arce
This paper proposes to use the a-priori knowledge of the target layout patterns to design data-adaptive compressive sensing (CS) methods for efficient source optimization (SO) in lithography systems. A set of monitoring pixels are selected from the target layout based on blue noise random patterns. The SO is then formulated as an under-determined linear problem to improve image fidelity according to the monitoring pixels. Adaptive projections are then designed, based on the a-priori knowledge of the target layout, in order to further reduce the dimension of the optimization problem, while trying to retain the SO performance...
March 20, 2017: Optics Express
https://www.readbyqxmd.com/read/28365843/real-time-detection-of-organic-contamination-events-in-water-distribution-systems-by-principal-components-analysis-of-ultraviolet-spectral-data
#15
Jian Zhang, Dibo Hou, Ke Wang, Pingjie Huang, Guangxin Zhang, Hugo Loáiciga
The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data...
May 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28343061/automated-diabetic-macular-edema-dme-grading-system-using-dwt-dct-features-and-maculopathy-index
#16
U Rajendra Acharya, Muthu Rama Krishnan Mookiah, Joel E W Koh, Jen Hong Tan, Sulatha V Bhandary, A Krishna Rao, Yuki Hagiwara, Chua Kuang Chua, Augustinus Laude
The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME...
March 19, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28324937/data-driven-estimation-of-blood-pressure-using-photoplethysmographic-signals
#17
Shi Chao Gao, Peter Wittek, Li Zhao, Wen Jun Jiang
Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28316861/complexity-analysis-of-electroencephalogram-dynamics-in-patients-with-parkinson-s-disease
#18
Guotao Liu, Yanping Zhang, Zhenghui Hu, Xiuquan Du, Wanqing Wu, Chenchu Xu, Xiangyang Wang, Shuo Li
In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages...
2017: Parkinson's Disease
https://www.readbyqxmd.com/read/28295824/wavelet-entropy-of-bold-time-series-an-application-to-rolandic-epilepsy
#19
Lalit Gupta, Jacobus F A Jansen, Paul A M Hofman, René M H Besseling, Anton J A de Louw, Albert P Aldenkamp, Walter H Backes
PURPOSE: To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. MATERIALS AND METHODS: The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated...
March 11, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28284000/a-method-for-microcalcifications-detection-in-breast-mammograms
#20
Abbas H Hassin Alasadi, Ahmed Kadem Hamed Al-Saedi
Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation...
April 2017: Journal of Medical Systems
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