INSTITUTE OF PHYSICS PUBLISHING PHYSIOLOGICAL MEASUREMENT
Physiol. Meas. 24 (2003) 1–15 PII: S0967-3334(03)58178-3
Time–frequency modelling and discrimination of noise
in the electrocardiogram
Piotr Augustyniak
Institute of Automatics, University of Science and Technology,
30 Mickiewicza Ave. 30-059 Krakow, Poland
Received 2 January 2003, in final form 9 June 2003
Published DD MMM 2003
Online at stacks.iop.org/PM/24/1
Abstract
In widely spread home care applications of ECG recorders, the traditional
approach to the problem of noise immunity is no longer sufficient. This paper
presents a new ECG-dedicated noise removal technique based on a time–
frequency noise model computed in a quasi-continuous way. Our algorithm
makes use of the local bandwidth variability of cardiac electrical representation
and splits the discrete time sequence into two sub-planes. The background
activities of any origin (muscle, power line interference, etc) are measured in
the regions of the time–frequency plane, situated above the local bandwidth
of the signal. The noise estimate on each particular scale is non-uniformly
sampled and needs to be extrapolated to the regions where the components of
cardiac representation are normally expected. On the lower scales, the noise
contribution is computed with the use of square polynomial extrapolation.
The time–frequency representation of noise, partially measured and partially
calculated, is arithmetically subtracted from the noisy signal, and the inverse
time–frequency transform yields a noise-free cardiac representation. The
algorithm was tested with the use of CSE database records with the addition
of MIT-BIH database noise patterns. The static and dynamic performance of
the algorithm is sufficient to ameliorate the signal-to-noise ratio by more than
11 dB.
Keywords: electrocardiography, noise removal techniques, time–frequency
domain
1. Introduction
Noise removal is very often addressed in biomedical signal recording techniques. The primary
reason is the unknown and unstable recording environment (unwanted signals, poor electrodes,
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