INVESTIGATION OF ECG SIGNAL USING DSP PROCESSOR AND WAVELETS

The analysis of the ECG has been widely used for diagnosing many cardiac
diseases. The ECG is a graphic record of the direction and magnitude of the electrical
activity that is generated by depolarization and repolarization of the atria and
ventricles. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. Most of
the clinically useful information in the ECG is found in the intervals and amplitudes
defined by its features. The development of accurate and quick methods for
automatic ECG feature extraction is of major importance, especially for the analysis
of long recordings (Holters and ambulatory systems). In fact, beat detection is
necessary to determine the heart rate, and several related arrhythmias such as
Tachycardia, Bradycardia and Heart Rate Variation; it is also necessary for further
processing of the signal in order to detect abnormal beats. The ECG feature
extraction system provides fundamental features (amplitudes and intervals) to be
used in subsequent automatic analysis.
The large number of known wavelet families and functions provides a rich
space in which to search for a wavelet which will very efficiently represent a signal of
interest in a large variety of applications. Wavelet families include Biorthogonal,
Coiflet, Harr, Symmlet, Daubechies wavelets, etc. There is no absolute way to choose
a certain wavelet. The choice of the wavelet function depends on the application. The
Haar wavelet algorithm has the advantage of being simple to compute and easy to
understand. The Daubechies algorithm is conceptually more complex and has a
slightly higher computational overhead. But, the Daubechies algorithm picks up
detail that is missed by the Haar wavelet algorithm. Even if a signal is not well
represented by
one member of the Db family, it may still be efficiently represented by another.
Selecting a wavelet function which closely matches the signal to be processed is of
utmost importance in wavelet applications.

In particular, the influence of the selection of wavelet function and the choice
of decomposition level on efficiency of denoising process were considered and whole
procedures of noise reduction is done in TMS320CXX processor in MatLab
environment. The Fast Wavelet Transform was use. The advantage of used denoising
method is noise level decreasing in ECG signals, in which noise reduction by
averaging has limited application, i.e. in case of arrhythmia, or in presence of
extrasystols.
Noise reduction in ECG signals is one of the main problems, which appear
during analysis of electrical activity of the heart. The most troublesome noise sources
contain frequency components within ECG spectrum, i.e.: electrical activity of
muscles (EMG), and instability of electrode-skin contact. Such noises are difficult to
remove using typical filtering procedures. Efficient analytical tool which allows to
increase signal to noise ratio is a technique of averaging of cardiac cycles.
Effectiveness of this method strictly depends on stable sinus rhythm. That
requirement is however not fulfilled in case of arrhythmia, or the presence of many
extra systoles. In such signals noise reduction is only possible with using, more
advanced signal processing method, as wavelet denoising technique.
The aim of this study is to investigate the application of DSP processor for real
time denoising in high resolution ECG signals. In this work, we are developing and
evaluated an electrocardiogram (ECG) feature extraction system based on the multiresolution
wavelet transform. In this work, we will develop and evaluated an
electrocardiogram (ECG) feature extraction system based on the multi-resolution
wavelet transform.