Real-time regurgitation estimation in percutaneous left ventricular assist device fully supported condition using an unscented Kalman filter.

Objective.Significant aortic regurgitation is a common complication following left ventricular assist device (LVAD) intervention, and existing studies have not attempted to monitor regurgitation signals and undertake preventive measures during full support. Regurgitation is an adverse event that can lead to inadequate left ventricular unloading, insufficient peripheral perfusion, and repeated episodes of heart failure. Moreover, regurgitation occurring during full support due to pump position offset cannot be directly controlled through control algorithms. Therefore, accurate estimation of regurgitation during percutaneous left ventricular assist device (PLVAD) full support is critical for clinical management and patient safety.Approach.An estimation system based on the regurgitation model is built in this paper, and the unscented Kalman filter estimator (UKF) is introduced as an estimation approach. Three offset degrees and three heart failure states are considered in the investigation. Using the mock circulatory loop experimental platform, compare the regurgitation estimated by the UKF algorithm with the actual measured regurgitation; the errors are analyzed using standard confidence intervals of ±2 SDs, and the effectiveness of the mentioned algorithms is thus assessed. The generalization ability of the proposed algorithm is verified by setting different heart failure conditions and different rotational speeds. The root mean square error and correlation coefficient between the estimated and actual values are quantified and the statistical significance of accuracy differences in estimation is illustrated using one-way analysis of variance (One-Way ANOVA), which in turn assessed the accuracy and stability of the UKF algorithm.Main results.The research findings demonstrate that the regurgitation estimation system based on the regurgitation model and UKF can relatively accurately estimate the regurgitation status of patients during PLVAD full support, but the effect of myocardial contractility on the estimation accuracy still needs to be taken into account.Significance.The proposed estimation method in this study provides essential reference information for clinical practitioners, enabling them to promptly manage potential complications arising from regurgitation. By sensitively detecting LVAD adverse events, valuable insights into the performance and reliability of the LVAD device can be obtained, offering crucial feedback and data support for device improvement and optimization.

Regurgitation during the fully supported condition of the percutaneous left ventricular assist device.

Objective.A percutaneous left ventricular assist device (PLVAD) can be used as a bridge to heart transplantation or as a temporary support for end-stage heart failure. Transvalvularly placed PLVADs may result in aortic regurgitation due to unstable pump position during fully supported operation, which may diminish the pumping effect of forward flow and predispose to complications. Therefore, accurate characterization of aortic regurgitation is essential for proper modeling of heart-pump interactions and validation of control strategies.Approach.In the present study, an improved aortic valve model was used to analyze the severity of regurgitation produced by different pump position offsets. The link between pump position offset degree and regurgitation is validated in the fixed speed mode, and the influence of pump speed on regurgitation is verified in the variable speed mode, using the mock circulatory loop (MCL) experimental platform.Main results.The greater the pump offset and the more severe the regurgitation, the more carefully the pump speed needs to be managed. To avoid over-pumping, the recommended pump speed in this study should not exceed 30 000 rpm.Significance.The modeling approach provide in this study not only makes it easier to comprehend the impact of regurgitation events on the entire interactive system during mechanical assistance, but it also aids in providing timely alerts and suitable management measures.

Wideband Filtering Slot Antenna Design with Stable Gain Using Characteristic Mode Analysis.

A filtering slot antenna with a simple structure combination using characteristic mode analysis (CMA) is proposed. To realize filtering characteristics, characteristic magnetic currents of line and ring slots are analyzed and designed. Then, the folding-line slot and double-ring slot are selected to realize radiation null separately and combined to construct the basic slot antenna. By properly exciting the selected characteristic modes, a wide filtering bandwidth and a stable gain are obtained. To validate the design process, a prototype antenna with a finite ground plane of about 1.1 λ × 1.1 λ is designed and fabricated. Simulated and measured results agree well, which both show a sharping roll rate in the lower and higher frequency and a flat gain realization in the pass band. The filtering bandwidth is 32.7%, the out-of-band suppression level at the higher frequency is over 20 dB, and the gain in the working frequency varies from 3.9 to 5.2 dB.

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning.

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo-Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert's experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

Frequency shift of the Bragg and Non-Bragg backscattering from periodic water wave.

Doppler effect is used to measure the relative speed of a moving target with respect to the radar, and is also used to interpret the frequency shift of the backscattering from the ocean wave according to the water-wave phase velocity. The widely known relationship between the Doppler shift and the water-wave phase velocity was deduced from the scattering measurements data collected from actual sea surface, and has not been verified under man-made conditions. Here we show that this ob- served frequency shift of the scattering data from the Bragg and Non-Bragg water wave is not the Doppler shift corresponding to the water-wave phase velocity as commonly believed, but is the water-wave frequency and its integral multiple frequency. The power spectrum of the backscatter from the periodic water wave consists of serials discrete peaks, which is equally spaced by water wave frequency. Only when the water-wave length is the integer multiples of the Bragg wave, and the radar range resolution is infinite, does the frequency shift of the backscattering mathematically equal the Doppler shift according to the water-wave phase velocity.

Wind turbine clutter mitigation in coastal UHF radar.

Coastal UHF radar provides a unique capability to measure the sea surface dynamic parameters and detect small moving targets, by exploiting the low energy loss of electromagnetic waves propagating along the salty and good conducting ocean surface. It could compensate the blind zone of HF surface wave radar at close range and reach further distance than microwave radars. However, its performance is susceptible to wind turbines which are usually installed on the shore. The size of a wind turbine is much larger than the wavelength of radio waves at UHF band, which results in large radar cross section. Furthermore, the rotation of blades adds time-varying Doppler frequency to the clutter and makes the suppression difficult. This paper proposes a mitigation method which is based on the specific periodicity of wind turbine clutter and performed mainly in the time-frequency domain. Field experimental data of a newly developed UHF radar are used to verify this method, and the results prove its effectiveness.