Multiple stopping criteria and high-precision EMD architecture implementation for Hilbert-Huang transform

Tsung Che Lu, Pei Yu Chen, Shih Wei Yeh, Lan-Da Van

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

5 Scopus citations

Abstract

In this work, a multiple stopping criteria and high-precision empirical mode decomposition (EMD) hardware architecture implementation is proposed for Hilbert-Huang transform (HHT) in biomedical signal processing. The proposed architecture can support multiple stopping criteria including the constant criteria, the SD criteria and the ratio criteria. The 38-bit floating point precision is adopted in this work to support 10 IMF components with enough accuracy. The off-chip memory architecture is adopted to increase the processing capacity. By the pipelined cubic spline coefficient unit (PCSCU), the computation time can be reduced. The proposed EMD hardware architecture is implemented in TSMC 90 nm CMOS process with the core area of 4.47 mm2 at the operating frequency of 40 MHz. The post-layout simulation result shows that our work with the constant criterion can speed up the performance 50.4 times compared to the software computation on a single core of ARM11 for 2K data size breathing signals.

Original languageEnglish
Title of host publicationIEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages200-203
Number of pages4
ISBN (Electronic)9781479923465
DOIs
StatePublished - 9 Dec 2014
Event10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014 - Lausanne, Switzerland
Duration: 22 Oct 201424 Oct 2014

Publication series

NameIEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings

Conference

Conference10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014
CountrySwitzerland
CityLausanne
Period22/10/1424/10/14

Fingerprint Dive into the research topics of 'Multiple stopping criteria and high-precision EMD architecture implementation for Hilbert-Huang transform'. Together they form a unique fingerprint.

Cite this