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To solve the difficulties in threshold selection and poor performance under low signal‐to‐noise ratio (SNR) conditions in existing spectrum sensing algorithms, a graph‐based spectrum sensing algorithm using nonlinear function regulation was proposed. The idea was to add a specific nonlinear transformation between the normalisation and quantization steps of the existing signal‐to‐graph converter (SGC). If the autocorrelation function of the observed signal selected as the input fed to SGC, the nonlinear function has the ability to adjust the uniformity of its probability distribution, increasing the probability of the observed signal being transformed into a complete graph under the alternative hypothesis, whereas remaining a noncomplete graph under the null hypothesis. Thus transformed the graph‐based spectrum sensing into a complete graph‐detection problem. Based on the theory of dispersive ordering, a theoretical analysis of the mechanism by which nonlinear transformations affect graph connectivity was conducted. The simulation results showed that the detection performance of the proposed algorithm was superior to that of existing graph‐based spectrum sensing algorithms. When SNR was −7 dB, the detection probability of the proposed algorithm exceeded 95%. Moreover, among the existing graph‐based spectrum sensing algorithms, the proposed algorithm exhibited the lowest computational complexity apart from the block range‐based method. [ABSTRACT FROM AUTHOR]

Background: Transcatheter aortic valve replacement (TAVR) is gradually becoming an alternative therapy for patients who cannot adapt to surgical treatment or have contraindications. Objectives: The purpose of this study was to investigate the value of echocardiography in the evaluation of severe AR patients treated with TAVR and to analyze the correlations with postprocedural complications to improve the evaluation and screening of patients. Methods: We retrospectively analyzed clinical and echocardiographic data of 70 patients with severe AR. Periaortic valve structures were carefully measured by esophageal echocardiography (TEE) and compared with the multilayer slice computed tomography (MSCT) findings. Real‐time three‐dimensional esophageal echocardiography (RT‐3D TEE) was monitored during the operation, and a 30‐day postprocedural follow‐up was performed. The relationship between postprocedural complications and patients' clinical data or periaortic valve structures was analyzed by multifactorial analysis to identify relevant predictors of complications. Results: The TEE measurements of periaortic valve structures were in good agreement with the MSCT measurements. Among the patients who underwent successful operations, both left atrial (LA) and left ventricular (LV) diameters were reduced, and the left ventricular ejection fraction (LVEF) was improved 30 days after TAVR compared with the preprocedural period (P <.05). Permanent pacemakers were implanted in 15 patients. The presence of preprocedural right bundle branch block (RBBB) (OR: 2.93; 95% CI: 1.18‐12.70; P =.01) was an independent factor for permanent pacemaker implantation after TAVR. Conclusions: Echocardiography plays an extremely important role in TAVR procedures. The presence of preprocedural RBBB can be an independent predictor of postprocedural pacemaker implantation. [ABSTRACT FROM AUTHOR]

Background: New-generation self-expanding valves can improve the success rate of transcatheter aortic valve replacement (TAVR) for severe pure native aortic regurgitation (PNAR). However, predictors of new-onset conduction block post-TAVR using new-generation self-expanding valves in patients with PNAR remain to be established. Therefore, this study aimed to identify predictors of new-onset conduction block post-TAVR using new-generation self-expanding valves (VitaFlow Liberty™) in patients with PNAR. Methods: In this retrospective cohort study, patients were categorized into pacemaker and non-pacemaker groups based on their need for new postoperative permanent pacemaker implantation (PPI). Based on the postoperative presence of either new-onset complete left bundle branch block (cLBBB) or high-grade atrioventricular block (AVB), patients were further classified into conduction disorder and non-conduction disorder groups. Laboratory, echocardiographic, computed tomography, preoperative and postoperative electrocardiography, and procedural and clinical data were collected immediately after TAVR and during hospitalization and compared between the groups. Multivariate logistic regression analysis was performed incorporating the significant variables from the univariate analyses. Results: This study examined 68 consecutive patients with severe PNAR who underwent TAVR. In 20 patients, a permanent pacemaker was fitted postoperatively. Multivariate logistic regression analysis revealed an association between the need for postoperative PPI and preoperative complete right bundle branch block (cRBBB) or first-degree AVB, as well as a non-tubular left ventricular outflow tract (LVOT). In addition, valve implantation depth and angle of aortic root were independent predictors of new-onset cLBBB or high-grade AVB developing post-TAVR. The predictive value of valve implantation depth and angle of aortic root was further supported by receiver operating characteristic curve analysis results. Conclusions: In patients with PNAR undergoing TAVR using self-expanding valves, preoperative cRBBB or first-degree AVB and a non-tubular LVOT were indicators of a higher likelihood of PPI requirement. Moreover, deeper valve implantation depth and greater angle of aortic root may be independent risk factors for new-onset cLBBB or high-grade AVB post-TAVR. Valve implantation depth and angle of aortic root values may be used to predict the possibility of new cLBBB or high-grade AVB post-TAVR. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low signal to noise ratio (SNR) and low probability of intercept (LPI) radar pulse waveform recognition accuracy, a radar emitter signal recognition method based on time-frequency analysis, squeeze-excitation (SE) and ResNeXt network is proposed. Firstly, the radar time domain signal is transformed into a two-dimensional time-frequency image (TFI) by Choi-Williams distribution (CWD); then, the TFI pre-processing is used to reduce the noise interference and the difference in frequency dimension location distribution, adapting to deep learning network input; finally, the TFI features are extracted by adding dilated convolution and SE structure on the basis of ResNeXt to achieve radar emitter classification. The experimental results show that when the SNR is as low as --8 dB, the overall recognition accuracy of the method for 12 types of common LPI radar waveforms can still reach 98. 08%. [ABSTRACT FROM AUTHOR]