01-01-2013, 11:26 AM
NOISE REMOVAL AND R-PEAK DETECTION OF ECG SIGNALS
NOISE REMOVAL.pdf (Size: 1.56 MB / Downloads: 76)
Introduction
Electrocardiogram (ECG) is a nearly periodic signal that reflects the activity of the heart. A lot
of information on the normal and pathological physiology of heart can be obtained from ECG.
However, the ECG signals being non-stationary in nature, it is very difficult to visually analyze
them. Thus the need is there for computer based methods for ECG signal Analysis.
A lot of work has been done in the field of ECG signal Analysis using various approaches and
methods. The basic principle of all the methods however involves transformation of ECG signal
using different transformation techniques including Fourier Transform, Hilbert Transform,
Wavelet transform etc. Physiological signals like ECG are considered to be quasi-periodic in
nature. They are of finite duration and non stationary. Hence, a technique like Fourier series
(based on sinusoids of infinite duration) is inefficient for ECG. On the other hand, wavelet,
which is a very recent addition in this field of research, provides a powerful tool for extracting
information from such signals. There has been use of both Continuous Wavelet Transform (CWT)
as well as Discrete Wavelet Transform (DWT). However CWT has some inherent advantages
over DWT. Unlike DWT, there is no dyadic frequency jump in CWT. Moreover, high resolution
in time-frequency domain is achieved in CWT [3].
Motivation
ECG reflects the state of cardiac heart and hence is like a pointer to the health conditions of a
human being. ECG, if properly analyzed, can provide us information regarding various diseases
related to heart. However, ECG being a non-stationary signal, the irregularities may not be
periodic and may show up at different intervals [9]. Clinical observation of ECG can hence
take long hours and can be very tedious. Moreover, visual analysis cannot be relied upon. This
calls for computer-based techniques for ECG analysis. Various contributions have been made
in literature regarding beat detection and classification of ECG [10] [11]. Most of these use
frequency or time domain representation of ECG signals. But the major problem faced by the
coders is the vast variations in the morphologies of ECG signals. Moreover, we have to consider
the time constraints as well. Thus our basic objective is to come up with a simple method having
less computational time without compromising with the efficiency.
This objective has motivated us to explore a more effective technique for cardiovascular disease
detection by analyzing electrocardiogram (ECG) signals .Heart wave commonly change their
statistical properties over time, tending to be non stationary for which EMD,EEMD and wavelet
transform is a powerful tool of decomposition for its efficiency and simplicity. Overall we have
tried to minimize the computational time and maximize the efficiency.
Heart
The heart, located in the mediastinum, is the central structure of the cardiovascular system. It is
protected by the bony structures of the sternum anteriorly, the spinal column posteriorly, and the rib
cage.
Sinoatrial (SA) node is the dominant pacemaker of the heart, located in upper portion of right
atrium. It has an intrinsic rate of 60–100 bpm.
Atrioventricular(AV) node is a part of AV junctional tissue. It slows conduction, creating a
slight delay before impulses reach ventricles. It has an intrinsic rate of 40–60 bpm [10].
Electrocardiogram (ECG)
An ECG is a series of waves and deflections recording the heart’s electrical activity from a
certain “view”. Many views, each called a lead, monitor voltage changes between electrodes
placed in different positions on the body.
Each cardiac cell is surrounded by and filled with solutions of Sodium (Na+), Potassium (K+),
and Calcium(Ca++). The interior of the cell membrane is considered to be negative with
respect to outside during resting conditions. When an electric impulse is generated in the heart,
the interior part becomes positive with respect to the exterior. This change of polarity is called
depolarization. After depolarization the cell comes back to its original state. This phenomenon is
called repolarization. The ECG records the electrical signal of the heart as the muscle cells
depolarize (contract) and repolarize. A normal ECG signal is shown in Fig.4.