14-01-2013, 04:16 PM
Stationary and Non-Stationary noise removal from Cardiac Signals using a Constrained Stability Least Mean Square Algorithm
Stationary.pdf (Size: 834.41 KB / Downloads: 274)
Abstract-
Adaptive filter is a primary method to filter
ECG signal, because it does not need the signal statistical
characteristics. In this paper we present a novel adaptive
filter for removing the artifacts from ECG signals based on
Constrained Stability Least Mean Square (CSLMS) algorithm.
This algorithm is derived based on the minimization of the
squared Euclidean norm of the difference weight vector under a
stability constraint defined over the posteriori estimation error.
The adaptive filter essentially minimizes the mean-squared
error between a primary input, which is the noisy ECG, and
a reference input, which is either noise that is correlated in
some way with the noise in the primary input or a signal
that is correlated only with ECG in the primary input.
Different filter structures are presented to eliminate the diverse
forms of noise. Finally, we have applied this algorithm on
ECG signals from the MIT-BIH data base and compared its
performance with the conventional LMS algorithm. The results
show that the performance of the CSLMS based algorithm
is superior to that of the LMS based algorithm in noise reduction.
INTRODUCTION
Baseline wander and powerline interference reduction is the
first step in all electrocardiographic (ECG) signal processing.
The baseline wander is caused by varying electrode-skin
impedance, patients movements and breath. This kind of disturbances
is especially present in exercise electrocardiography,
as well as during ambulatory and Holter monitoring. The ECG
signal is also degraded by additive 50 or 60 Hz powerline (AC)
interference. This kind of disturbance can be modeled by a
sinusoid with respective frequency and random phase. These
two artifacts are the dominant artifacts and strongly affects the
ST segment, degrades the signal quality, frequency resolution,
produces large amplitude signals in ECG that can resemble
PQRST waveforms and masks tiny features that are important
for clinical monitoring and diagnosis. Cancelation of these
artifacts in ECG signals is an important task for better diagnosis.
Hence the extraction of high-resolution ECG signals from
recordings which are contaminated with background noise is
an important issue to investigate. The goal of ECG signal
enhancement is to separate the valid signal components from
the undesired artifacts, so as to present an ECG that facilitates
easy and accurate interpretation.
SIMULATION RESULTS
To show that CSLMS algorithm is really effective in clinical
situations, the method has been validated using several ECG
recordings with a wide variety of wave morphologies from
MIT-BIH arrhythmia database. We used the benchmark MITBIH
arrhythmia database ECG recordings as the reference for
our work and real noise is obtained from MIT-BIH Normal
Sinus Rhythm Database (NSTDB). In our simulations we
consider both stationary (PLI) and non-stationary (BW) noises.
The arrhythmia data base consists of 48 half hour excerpts of
two channel ambulatory ECG recordings, which were obtained
from 47 subjects, including 25 men aged 32-89 years, and
women aged 23-89 years. The recordings were digitized at
360 samples per second per channel with II-bit resolution
over a 10 m V range. In our experiments we used a data set of
five records (records 101, 102, 103, 104 and 105) but due to
space constraint simulation results for record 105 are shown
in this paper.