NDLI: The combination of Kaiser window and moving average for the low-pass filtering of the remote ECG signals

Content Provider	IEEE Xplore Digital Library
Author	Ying-Wen Bai Wen-Yang Chu Chien-Yu Chen Yi-Ting Lee Yi -Ching Tsai Cheng-Hung Tsai
Copyright Year	2004
Description	Author affiliation: Dept. of Electron. Eng., Fe Jun Catholic Univ., Taipei, Taiwan (Ying-Wen Bai; Wen-Yang Chu; Chien-Yu Chen; Yi-Ting Lee; Yi -Ching Tsai; Cheng-Hung Tsai)
Abstract	After an analog ECG signal is transferred into digital format, a suitable digital filter can be used to suppress the high-frequency embedded noise. In this paper, we use the equiripple FIR low-pass filter by superimposing of the optimal method, the Butterworth IIR low-pass filter, the 8-point moving-average filter and the FIR filter designed by using a Kaiser window. Furthermore, we combine the 8-point moving-average filter with the FIR filter designed by using a Kaiser window. In addition, we use the mean square error (M. S. E.) to estimate the effect of the digital filters in order to compare the reduction of the embedded high-frequency noise. Hence, we compute the mean square error with respect to the order, N, of these filters and plot the relationship between M. S. E. and N. Finally, we find the relationship between the CPU time and N.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Medicine Texas Tech Univ. College of Eng
Starting Page	273
Ending Page	278
File Size	300383
Page Count	6
File Format	PDF
ISBN	0769521045
ISSN	10637125
DOI	10.1109/CBMS.2004.1311727
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Low pass filters Filtering Electrocardiography Finite impulse response filter IIR filters Digital filters Digital signal processing Circuit noise Mean square error methods Noise reduction
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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