By classifying the heart sound signals, it can provide very favorable clinical information to the diagnosis of cardiovascular diseases. According to the characteristics of heart sound signals which are complex and difficult to classify and recognize, a new method of feature extraction and classification about heart sound signal is proposed by a combination of wavelet scattering transform and twin support vector machine in this paper. The method is as follows: The heart sound signal data set is firstly divided into two parts, one as a training set and the other as a testing set. Then the wavelet scattering transform is applied to the heart sound signals in the training set and the testing set. The scattering transform is a new timefrequency analysis method. It overcomes the shortcomings of the traditional wavelet transform which has the time-shift changes. It has the advantages of translation invariance and elastic deformation stability. Thus obtain the scattering feature matrix of the heart sound signal. Due to the large dimension of scattering feature matrix, this paper uses multidimensional scaling (MDS) method to reduce the dimension. This method is compared with the classical dimension reduction method-principal component analysis (PCA). Finally, the dimensionality-reduced feature matrix is input into the twin support vector machine (TWSVM) for training. After training the classifier to get the optimal parameters, the dimensionality-reduced scattering feature matrix of the testing signal is input into the classifier for testing. Experimental results show that the classification accuracy of the proposed method can reach 98% or more, and the running time is greatly reduced compared with support vector machine (SVM). INDEX TERMS Wavelet scattering transform, multidimensional scaling (MDS), twin support vector machine (TWSVM), signal classification.
Magnetic induction tomography (MIT) is a non-invasive modality for imaging the complex conductivity (σ) or the magnetic permeability (μ) of a target under investigation. The critical issue in the clinical application of the detection of cerebral hemorrhage is the determination of intracranial hematoma status, including the location and volume of intracranial hematoma. In MIT, the reconstruction image is used to reflect intracranial hematoma. However, in medical applications where high resolutions are sought, image reconstruction is a time-and memory-consuming task because the associated inverse problem is nonlinear and illposed. The reconstruction image is the result of a series of calculations on the boundary detection value, and the color of the reconstructed image is the relative value. To quantitatively and faster represent intracranial hematoma and to provide a variety of characterization methods for MIT dynamic monitoring, one-dimensional quantitative indicators are established. Our experiment results indicate that there is a linear relationship between one-dimensional quantitative indicators. The change of the detection value can roughly determine the location of the hematoma.
Background: Pyroptosis is an inflammatory form of cell death triggered by certain inflammasomes. Accumulating studies have shown the involvement of pyroptosis in the proliferation, invasion, and metastasis and prognosis of cancer. The prognostic value of pyroptosis-related genes (PRGs) and their association with immune infiltration in bladder cancer have not yet been elucidated.Methods: We performed a comprehensive analysis of the prognostic value and immune infiltrates of PRGs in bladder cancer using the TCGA dataset. qRT-PCR was also performed to verify our result.Results: Among 33 PRGs, 14 PRGs were upregulated or downregulated in bladder cancer tissue versus normal tissue. We also summarized copy number variations and somatic mutations of PRGs in bladder cancer. By using consensus clustering analysis of PRGs with prognostic significance, we divided the bladder cancer cohort into two subtypes significantly by different prognosis and immune infiltration. Using the LASSO Cox regression analysis, a prognostic signature including six PRGs was constructed for bladder cancer and the patients could be classified into a low- or high-risk group. Interestingly, this prognostic signature had a favorable performance for predicting the prognosis of bladder cancer patients. Moreover, further analysis demonstrated a significant difference in gender, tumor grade, clinical stage, TNM stage, immunoScore, and immune cell infiltration between the high- and low-risk groups in bladder cancer. We also identified an lncRNA SNHG14/miR-20a-5p/CASP8 regulatory axis in bladder cancer by constructing a ceRNA network.Conclusion: We identified a PRG-associated prognostic signature associated with the prognosis and immune infiltrates for bladder cancer and targeting pyroptosis may be an alternative approach for therapy. Further vivo and vitro experiments are necessary to verify these results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.