Processing of pulse oximeter data using discrete wavelet analysis

Seungjoon Lee; Bennett L. Ibey; Weijian Xu; Mark A. Wilson; M. Nance Ericson; Gerard L. Coté

doi:10.1109/TBME.2005.847538

Processing of pulse oximeter data using discrete wavelet analysis

Seungjoon Lee, Bennett L. Ibey, Weijian Xu, Mark A. Wilson, M. Nance Ericson, Gerard L. Coté

Sensors and Electronics

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.

Original language	English
Pages (from-to)	1350-1352
Number of pages	3
Journal	IEEE Transactions on Biomedical Engineering
Volume	52
Issue number	7
DOIs	https://doi.org/10.1109/TBME.2005.847538
State	Published - Jul 2005

Funding

Manuscript received October 24, 2003; revised November 27, 2004. This work was supported in part by the U.S. Department of Energy under Grant KP1402010. Asterisk indicates corresponding author. S. Lee and B. L. Ibey are with the Department of Biomedical Engineering, Texas A&M University, Biomedical Engineering, College Station, TX 77843 USA (e-mail: [email protected]; [email protected]). W. Xu is with Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213 USA (e-mail: [email protected]). M. A. Wilson is with Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15240 USA (e-mail: [email protected]). M. Nance Ericson is with Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA (e-mail: [email protected]). *G. L. Coté is with the Department of Biomedical Engineering, Texas A&M University, Mail Stop 3120, College Station, TX 77843 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TBME.2005.847538

Keywords

Artificial organ
DWT
FFT
Perfusion
Pulse oximeter
Wavelet

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10.1109/TBME.2005.847538

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abstract = "A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.",

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AU - Coté, Gerard L.

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AB - A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.

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Processing of pulse oximeter data using discrete wavelet analysis

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