27-02-2013, 12:20 PM
DENOISING ECG SIGNAL USING ADAPTIVE FILTER ALGORITHM
DENOISING ECG SIGNAL.pptx (Size: 784.84 KB / Downloads: 44)
ABSTRACT :
One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields, random body movements and respiration.
Adaptive filter technique is required to overcome this problem. In this paper type of adaptive filters are considered to reduce the ECG signal noises like PLI and Base Line Interference.
Results of simulations in LABVIEW are presented. In this we have used Recursive Least Squares (RLS). RLS algorithm is proposed for removing artifacts preserving the low frequency components and tiny features of the ECG.
INTRODUCTION
The extraction of high-resolution ECG signals from recordings contaminated with background noise is an important issue to investigate. The goal for 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. Many approaches have been reported in the literature to address ECG enhancement using adaptive filters [1]-[3], which permit to detect time varying potentials and to track the dynamic variations of the signals. In [4]-[6] proposed an LMS based adaptive recurrent filter to acquire the impulse response of normal QRS complexes, and then applied it for arrhythmia detection in ambulatory ECG recordings. The reference inputs to the LMS algorithm are deterministic functions and are defined by a periodically extended, truncated set of orthonormal basis functions.
PRINCIPLE : FOURIER SERIES
Any periodic functions which satisfy Dirichlet‟s Condition can be expressed as a series of scaled magnitudes of sin and cos terms of frequencies which occur as a multiple of fundamental frequency
ECG signal is periodic with fundamental frequency determined by the heart beat. It also satisfies the Dirichlet‟s Condition.
Single valued and finite in the given interval
Absolutely integrals
Finite number of maxima and minima between finite intervals
It has finite number of discontinuities
Hence Fourier series can be used for representing ECG signal.
CALCULATION:
If we observe figure1, we may notice that a single period of a ECG signal is a mixture of triangular and sinusoidal wave forms. Each significant feature of ECG signal can be represented by shifted and scaled versions one of these waveforms as shown below.
QRS, Q and S portions of ECG signal can be represented by triangular waveforms
P, T and U portions can be represented by triangular waveforms
Once we generate each of these portions, they can be added finally to get the ECG signal. Let‟s take QRS waveform as the centre one and all shifting takes place with respect to this part of the signal.
NOISE CANCELLATION :
The combined signal and noise form the "primary input" to the canceller. A second sensor receives a noise n1, which is uncorrelated with the signal but correlated in some unknown way with the noise n0. This sensor provides the "reference input” to the canceller. The noise n1 is filtered to produce an out put „y‟ that is a close replica of n0. This output is subtracted from the primary input „s+n0‟ to produce the system output, s+n0-y. If one knew the characteristics of the channels over which the noise wastransmitted to the primary and reference sensors, one could, in general, design a fixed filter capable of changing n1 into y= n0