Share Email Print

Proceedings Paper

A comparison of nonlinear noise reduction and independent component analysis using a realistic dynamical model of the electrocardiogram
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Accurate performance metrics for removing noise from the electrocardiogram (ECG) are difficult to define due to the inherently complicated nature of the noise and the absence of knowledge about the underlying dynamical processes. By using a previously published model for generating realistic artificial ECG signals and adding both stochastic and deterministic noise, a method for assessing the performance of noise reduction techniques is presented. Independent component analysis (ICA) and nonlinear noise reduction (NNR) are employed to remove noise from an ECG with known characteristics. Performance as a function of the signal to noise ratio is measured by both a noise reduction factor and the correlation between the cleaned signal and the original noise-free signal.

Paper Details

Date Published: 25 May 2004
PDF: 11 pages
Proc. SPIE 5467, Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems II, (25 May 2004); doi: 10.1117/12.548726
Show Author Affiliations
Patrick E. McSharry, University of Oxford (United Kingdom)
Gari D. Clifford, Harvard-MIT Div. of Health Sciences and Technology (United States)

Published in SPIE Proceedings Vol. 5467:
Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems II
Derek Abbott; Sergey M. Bezrukov; Andras Der; Angel Sanchez, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?