Previous studies have shown that the acoustic characteristics of coronary heart disease (CHD) are the diastolic turbulent murmurs. But there is little research on how to distinguish the differences between diastolic turbulent murmurs and diastolic valvular murmurs. In order to improve the diagnosis accuracy of CHD based on the analysis of diastolic murmurs, empirical wavelet transform was applied to distinguish the difference of diastolic murmurs between in CHD and in valvular disease. Firstly, the spectrum of diastolic heart sounds was divided into three modes and the segmentation boundaries were set as [150, 200] Hz. Next, the diastolic modal spectrums were obtained. It is found that the essential difference of diastolic murmurs between in CHD and in mitral stenosis is the third modal spectrum. Finally, the characteristic parameter P3 was defined and used to distinguish the diastolic murmurs in CHD and in valvular disease. The performance of the proposed method is tested and the results show when the proposed method was used to identify the diastolic murmurs of CHD, Se is 94.7%, Pp is 93.4% and Oa is 88.7%; When the proposed method identifying the diastolic murmurs of valvular disease, Se is 93.3%, Pp is 94.6% and Oa is 88.6%.

It is expected that an automatic detection and classification algorithm for the abnormities of first heart sound (S1) can realize computer artificial intelligence diagnosis of some relative cardiovascular disease. Few studies have focused on the detection and classification of the abnormities of S1 and given out in detail the essential differences between abnormal and normal S1. This work applied Empirical Wavelet Transform (EWT) to decompose S1 and extracted the instantaneous frequency (IF) of mitral component (M1) and tricuspid component (T1) by using Hilbert Transform. Firstly, the heart sound signal is preprocessed following these processes: filtering, resampling, normalization and segmentation. Secondly, S1 is decomposed into several modes based on EWT. First two maximal points with a distance greater than 20Hz in Fourier Spectrum of S1 are selected and the nearest minimal points on both sides of the maximal points are found out as the boundaries for segmentation of the spectrum. S1 is decomposed into 5 modes and every mode’s IF are calculated through Hilbert transformation. At last, a k-mean cluster algorithm is applied to cluster the IF of different modes. TD and Apeak_ratio are calculated for decision tree classifier and S1s are divided into three categories: normal S1, S1 with abnormal split and S1with abnormal amplitude change. When the proposed method is applied to detect normal S1, Se=94.6%, Pp=98.6% and Oa=93.3%; When it is applied to detect S1 with abnormal split, Se=92.6%, Pp=96.9% and Oa=90%; When it is applied to detect S1 with abnormal amplitude change, Se=94.4%, Pp=95.7% and Oa=90.6%; Comparison experiments are carried out between the proposed method and HVD method. The results show Oa of the proposed method is higher than HVD method when detecting the three different S1s.

Most researchers use features of diastolic murmurs to identify coronary artery disease. However, the diastolic murmurs of coronary artery disease are usually very weak and are easily contaminated by noise and valvular murmurs. Therefore, the diagnostic accuracy of coronary artery disease when only using diastolic murmurs is not well. An algorithm for improving the accuracy in the identification of coronary artery disease by combining the features of the first heart sound and diastolic murmurs was proposed. Firstly, a first heart sound feature extraction algorithm was used to identify coronary artery disease from noncoronary artery disease. Secondly, the Empirical Wavelet Transform algorithm was used to decompose the diastolic heart sound into three modes, and the spectral energy of each mode was calculated to distinguish coronary artery disease from noncoronary artery disease. Then, the features of the fist heart sound, the second diastolic spectral energy, and the parameter P3, which was used to discriminate the diastolic murmurs in coronary artery disease and in valvular disease, were combined together to improve the diagnostic accuracy of coronary artery disease. The comparison experiment results show that the accuracy of the proposed algorithm is superior to some state‐of‐the‐art methods when they are used to diagnose coronary artery disease.

This work proposes a polarization-insensitive scalable wide-angle metasurface array for highly efficient ambient energy harvesting in the 5.8 GHz Wi-Fi band. Inspired by dipole antenna principles, we design an asymmetric planar orthogonal dipole-based metasurface featuring monolithic integration of Schottky diodes (HSMS-2860) at unit cell feed gaps. This novel direct-impedance-matching strategy eliminates conventional matching networks, reducing energy conversion losses while enabling 99% radiation-to-AC efficiency across all polarization angles at 5.8 GHz. The coplanar architecture interconnects metasurface unit cells via inductors, simultaneously establishing low-loss DC channels and suppressing RF leakage. Fabricated as a 5 × 5 array, the prototype achieves 77.9% peak RF-to-DC efficiency with polarization insensitivity at an incident power of 25 dBm. Furthermore, with incident powers of 15 dBm and 20 dBm, the proposed metasurface array attained RF-to-DC conversion efficiencies exceeding 40% and 60%, respectively. This result indicates that the array is capable of achieving high energy harvesting efficiency across a broad power range. This scalable, drill-free, and polarization-insensitive design demonstrates strong potential for harvesting ambient RF energy in real-world multipath environments.