Window-dependent bases for efficient representations of the Stockwell transform

Battisti, Ubertino; Riba, L.

doi:10.1016/j.acha.2015.02.002

Cited by 50 publications

(39 citation statements)

References 25 publications

(26 reference statements)

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“…We experimentally found (using the defined training set) that we should fix the mean weight thresholds (μ 4 , μ 5 ) and the weights applied to the total variations (γ 4 th frequency band when adjacent phase differences are larger than μ 4 -times its mean: γ 4 = 2.8 -Weight applied to differences of the 5 th frequency band when adjacent phase differences are larger than μ 5 -times its mean: γ 5 = 2.8…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, this is another advantage of the ST, along with the fact that it does not present cross-terms (unlike the Choi-Williams and Wigner-Ville transforms) [27]. Unfortunately, ST presents high computational cost and memory requirements, which represent an important drawback when computing large signals [4]. It is for this reason that Brown et al [6] presented a computationally efficient implementation of the ST called the general Fourier-family transform (GFT), which minimizes computational time and resources making possible its application for biomedical signal processing [5].…”

Section: Time-frequency Transformsmentioning

confidence: 99%

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Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Ortigosa

Fernández

Galbis

et al. 2015

Biomedical Engineering / Biomedizinische Technik

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Abstract:Atrial fibrillation, which is the most common cardiac arrhythmia, is typically classified into four clinical subtypes: paroxysmal, persistent, long-standing persistent and permanent. The ability to distinguish between them is of crucial significance in choosing the most suitable therapy for each patient. Nevertheless, classification is currently established once the natural history of the arrhythmia has been disclosed as it is not possible to make an early differentiation. This paper presents a novel method to discriminate persistent and long-standing atrial fibrillation patients by means of a time-frequency analysis of the surface electrocardiogram. Classification results provide approximately 75% accuracy when evaluating ECGs of consecutive unselected patients from a tertiary center and higher than 80% when patients are not under antiarrhythmic treatment or do not have structural heart disease (76% sensitivity and 88% specificity). Moreover, to our knowledge, this is the first study that discriminates between persistent and long-standing persistent subtypes in a heterogeneous population sample and without discontinuing antiarrhythmic therapy to patients. Thus, it can help clinicians to address the most suitable therapeutic approach for each patient.

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Section: Resultsmentioning

confidence: 99%

Section: Time-frequency Transformsmentioning

confidence: 99%

Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Ortigosa

Fernández

Galbis

et al. 2015

Biomedical Engineering / Biomedizinische Technik

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“…50 DOST preserves the phase information while avoiding redundant calculations of time-frequency information and therefore being computationally less expensive. 50,51 It has been shown that DOST potentially provides a better approximation of the image information compared to wavelet and fourier transforms, 52 with higher SNR over base line compression. DOST is known to be one of the best techniques for image texture characterization, and outperforms the discrete wavelet transform (DWT) method.…”

Section: G4 Discrete Orthonormal Stock-well Transform (Dost) Featmentioning

confidence: 99%

“…DOST is known to be one of the best techniques for image texture characterization, and outperforms the discrete wavelet transform (DWT) method. 52 A two-dimensional DOST algorithm was applied to CBCT and pCT datasets in order to estimate the matrix of DOST coefficients for each slice of the tumor. As shown in Table II the DOST coefficient matrix was divided into nine equal segments.…”

Section: G4 Discrete Orthonormal Stock-well Transform (Dost) Featmentioning

confidence: 99%

On the Impact of Smoothing and Noise on Robustness of CT and CBCT Radiomics Features for Patients with Head and Neck Cancers

Bagher‐Ebadian

Liu

Siddiqui

et al. 2017

International Journal of Radiation Oncology*Biology*Physics

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Purpose: We investigated the characteristics of radiomics features extracted from planning CT (pCT) and cone beam CT (CBCT) image datasets acquired for 18 oropharyngeal cancer patients treated with fractionated radiation therapy. Images were subjected to smoothing, sharpening, and noise to evaluate changes in features relative to baseline datasets. Methods: Textural features were extracted from tumor volumes, contoured on pCT and CBCT images, according to the following eight different classes: intensity based histogram features (IBHF), gray level run length (GLRL), law's textural information (LAWS), discrete orthonormal stockwell transform (DOST), local binary pattern (LBP), two-dimensional wavelet transform (2DWT), Two dimensional Gabor filter (2DGF), and gray level co-occurrence matrix (GLCM). A total of 165 radiomics features were extracted. Images were post-processed prior to feature extraction using a Gaussian noise model with different signal-to-noise-ratios (SNR = 5,10,15,20,25,35,50,75, 100, and 150). Gaussian filters with different cut off frequencies (varied discreetly from 0.0458 to 0.7321 cycles-mm À1 ) were applied to image datasets. Effect of noise and smoothing on each extracted feature was quantified using mean absolute percent change (MAPC) between the respective values on post-processed and baseline images. The Fisher method for combining Welch P-values was used for tests of significance. Three comparisons were investigated: (a) Baseline pCT versus modified pCT (with given filter applied); (b) Baseline CBCT versus modified CBCT, and (c) Baseline and modified pCT versus baseline and modified CBCT. Results: Features extracted from CT and CBCT image datasets were robust to low-pass filtering

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“…In [1], it is proven that the so called DOST-functions, P j,k,τ (t) = T τ 2 j 1 2 j/2 j∈Z j,k e 2πi ηt , j ∈ N, τ = 0, . .…”

Section: Introductionmentioning

confidence: 99%

Stockwell-like frames for Sobolev spaces

Battisti

Berra

Tabacco

2018

J. Pseudo-Differ. Oper. Appl.

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We construct a family of frames describing Sobolev norm and Sobolev seminorm of the space H s (R n ). Our work is inspired by the Discrete Orthonormal Stockwell Transform introduced by R.G. Stockwell, which provides a time-frequency localized version of Fourier basis of L 2 ([0, 1]). This approach is a hybrid between Gabor and Wavelet frames. We construct explicit and computable examples of these frames, discussing their properties. 1 arXiv:1605.08691v1 [math.FA]

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Window-dependent bases for efficient representations of the Stockwell transform

Cited by 50 publications

References 25 publications

Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms

On the Impact of Smoothing and Noise on Robustness of CT and CBCT Radiomics Features for Patients with Head and Neck Cancers

Stockwell-like frames for Sobolev spaces

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