An advanced hardware design based on ensemble empirical mode decomposition algorithm for heart sound signal processing

Chia Ching Chou, Kuen Chih Lin, Wai-Chi  Fang, Arvin Huang Te Li, Yu Ching Chang, Bai Kuang Hwang, Yio Wha Shau

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In 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.

Original languageEnglish
Title of host publication2013 IEEE 17th International Symposium on Consumer Electronics, ISCE 2013
Pages37-38
Number of pages2
DOIs
StatePublished - 11 Sep 2013
Event2013 IEEE 17th International Symposium on Consumer Electronics, ISCE 2013 - Hsinchu, Taiwan
Duration: 3 Jun 20136 Jun 2013

Publication series

NameProceedings of the International Symposium on Consumer Electronics, ISCE

Conference

Conference2013 IEEE 17th International Symposium on Consumer Electronics, ISCE 2013
CountryTaiwan
CityHsinchu
Period3/06/136/06/13

Keywords

  • Ensemble Empirical Mode Decomposition
  • Signal processing

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