Chou, C.-C.C.-C.ChouLin, K.-C.K.-C.LinFang, W.-C.W.-C.FangLi, A.H.-T.A.H.-T.LiChang, Y.-C.Y.-C.ChangHwang, B.-K.B.-K.HwangShau, Y.-W.Y.-W.ShauYIO-WHA SHAU2020-04-282020-04-282013https://scholars.lib.ntu.edu.tw/handle/123456789/486479In this study, an advanced hardware design for heart sound signal processing based on ensemble empirical mode decomposition (EEMD) is developed and implemented. The EEMD method [1] is developed to alleviate a key drawback in the original empirical mode decomposition (EMD) algorithm. In a previous research, Huang et al. [2] developed an adaptive and efficient EMD method for nonlinear and nonstationary signal analysis. The physical meaning of a single intrinsic mode function (IMF) is obscure, and the original EMD algorithm cannot separate signals with different scales into appropriate IMFs. To overcome this major drawback, a noise-assisted data analysis (NADA) method called EEMD is developed. Heart sound signals are fed into the proposed system to simulate the EEMD-fixed-point performance. A comparison of the floating-point and fixed-point results exhibits satisfactory consistency and demonstrates that our design can accommodate wide variations of dynamic ranges and complicated calculations.conference paper10.1109/ISCE.2013.65702412-s2.0-84883527038https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883527038&doi=10.1109%2fISCE.2013.6570241&partnerID=40&md5=ceddbd13548a6f2774075c3ad4b913f3