Efficient Wavelet Based Pattern Matching Scheme For ECG Data Compression Assignment Solution

INTRODUCTION

This work utilizes wavelet analysis, a relatively new mathematical tool, to develop a new method of analysis for bio medical data acquired from human beings. Techniques which are currently employed fall into two main categories: time domain or the integral transform domain. Time domain methods include the mathematical solution of differential equations and companion circuit techniques. The integral transform domain methods include Laplace transform and frequency (e.g., Fourier transform) analysis. Both of the aforementioned categories can be stressed when solving systems of equations with a wide Eigen spectrum or when a system of equations is subjected to a non stationary forcing function. One of the benefits of wavelet analysis, however, is the ability to easily resolve signals of a non stationary nature.

PROBLEM STATEMENT

The following are the practical problems encountered while acquiring bio medical data. We cannot continuously monitor the physiological data through PC or any other measuring instruments like CRO.Also it is impossible to find the instant of such abnormalities in data through conventional methods. If the data has very small change in the time information, with conventional transforms we will not be able to detect the fault. 

Noise elimination involves the methods like digital filtering using some algorithms. These algorithms lack in the detection of the noise occurrence instant. This property is called as time localization. Usually any compression technique simply gives advantages of saving bandwidth and less memory storage, but lacks in analyzing the time relationship with frequency (Localization of time). No other method like neural network, fuzzy logic control and any other means require huge algorithms for time localization. Also it involves a big confusion while grouping the similar data. 

1.2 PROJECT OBJECTIVE

To get the efficient ECG signal, then it is compressed by using wavelet transformation. In this process we seen the periods of beats are normalized by multirate processing, amplitude normalization afterwards discrete wavelet transform is applied to each normalized beat, due to amplitude normalization, the wavelet transform co-efficient bear a high correlation across beats. To increase the compression ratio pattern matching unit is utilized .At the decoder ,the inverse wavelet transform is applied then we get  the reconstructed wavelet transform co-efficient .The original amplitude and period of each beat are then recovered.

1.3 PROJECT SCOPE

The project can be enhanced by means of detection automatically and can be used as a powerful tool to continuously monitor the patient’sstatus. Since time localization is there we can even perform the crime analysis because the instant is recorded in wavelet. Since the compression scheme is available in this project we may use it where a huge data base has to be analyzed. Any symptoms of the seriousness of the heart patient can be detected based on the width of the QRS pulse which can be found out using wavelet.

1.4 PROJECT OUTLINE

Chapter1 consist of introduction of the project, problem statement, project objective, and project scope. Chapter2 literature review explains the digital signal processing techniques and mat lab basics.Chapter3 explains the algorithms.Chapter4   explains the design methods using diagrams.Chapter5 is the results and result analysis of results. Chapter6 contains the conclusion of the project.

LITERATURE REVIEW

It explains the DIGITAL SIGNAL PROCESSING, MATLAB, ELECTROCARDIOGRAM SIGNAL (ECG), and also givesthe detailed description over the WAVELET TRANSFORM. In description of mat lab it explains about themat lab language, designing, loading and saving variables in mat lab. While in description of ECG signal, it can also represent theECG signal.

2.1. Digital signal processing

Digital signal processing (DSP) has come a long way in 20 years. As late as the early 1980s, it still needed to be done on mainframe computers. Research work was done by digitizing    signals for analysis in complex computer analysis runs that might return a result a day later. Scientists wrote their own programs or used libraries of programs written specifically to do analysis with. In fact, digital signal processing was used as a way to test filter designs for new electronic circuits. At that time, no one expected that computers would get faster and smaller as quickly as they did.

If we strip away the DIGITAL from Digital Signal Processing, we are left with something that we’ve been doing in electronics since it was first invented, Signal Processing! Signal processing is all about taking a signal, applying some change to it, and then getting a new signal out. That change might be amplification or filtration or something else, but nearly all electronic circuits can be considered to be signal processors. Looked on in this way, the signal processor as a black box might be composed of discrete components like capacitors and resistors, or it could be a complex integrated circuit with many circuits to accomplish a more complex task, or it could be a digital system which accepts a signal on its input and outputs the changed signal. So long as it accomplishes its defined task, it doesn’t matter how the box works internally.

Digital signal processors, even single chip DSP systems, are built from these elements. Twenty years ago, anyone using DSP had to be quite a mathematician to be able to implement and use the algorithms. Today, DSP can be incorporated into devices so simple that they can be mass-produced and operated with as little as the press of a button.

The internal programming of a DSP chip is far too complex to deal with here. It is generally proprietary as well, but the basics of how DSP works are simple enough to understand. While you won’t be able to implement your own algorithms after reading this, you will know a little more about how DSP works.

Sampling
Digital systems don’t work with continuous waveforms. Much work was done trying to create analog computers to handle calculations on continuous systems, but analog computers proved to be inflexible, slow, and hard to reconfigure to new tasks. In particular, it was hard to implement general algorithms on them. Maybe if digital systems had not developed so fast through the 1950s and 1960s, we might have solved the problems, but by the 1960s, it was already rare to find an analog computer anywhere. Serious work was done on digital computers where complex algorithms could be coded relatively easily. This meant that we had to get our data into digital form.

In the 1960s, digitizing data was often a manual task. As late as the 1980s, much data was read into digital computers from paper tape systems. However, increasingly computers were being harnessed directly to circuits that could produce a digital output when given an analog input. It became possible to take a signal such as the one below:

Once in the computer, the process could be reversed by playing it out through a D/A converter to produce a close approximation of the original waveform.  Notice the words ‘close approximation’. No digital sampling system can perfectly reproduce the original signal because each sample is a single number representing the signal in some small, but finite interval. 

Consider again the signal at the beginning of this note. It was made from two pure tones plus a random noise element. With block processing, we can apply a Fast Fourier Transform to the digitized signal to get an output that looks like this:

A Fourier Transform is a special mathematical algorithm that transforms the signal into a representation we can think of as the energy in the signal vs. frequency. In this case, we can see that most of the energy is concentrated in two single frequencies and the rest is spread out randomly across the spectrum. The noise is that random element. An Inverse Fourier Transform can return the signal back to its original time sampled form, called the time domain.

2.2. MATLAB

MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visa Alization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and FORTRAN.

MATLAB has a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas.

2.2.1 The MATLAB Language

The MATLAB language supports the vector and matrix operations that are fundamental to engineering and scientific problems. It enables fast development and execution. With  the  MATLAB  language, you  can  program  and  develop  algorithms  faster  than with  traditional  languages  because  you  do  not need to perform low-level administrative tasks, such as declaring variables, specifying data types, and allocating memory. 

In many cases, MATLAB eliminates the need for ‘for’ loops. At  the  same  time,  MATLAB   provides  all  the  features  of  a  traditional programming language, including arithmetic operators, flow control, data structures, data types, object-oriented programming (OOP), and debugging features.

A communications modulation algorithm that generates 1,024 random bits, performs modulation, adds complex Gaussian noise, and plots the result--all in just 9 lines of MATLAB code is as follows.

2.2.2 Development Tools

MATLAB includes development tools that help you implement your algorithm efficiently. 

MATLAB Editor - Provides standard editing and debugging features, such as set   break point.

Lint Code Checker - Analyzes your code and recommends changes to improve its performance.   

MATLAB Profiler - Records the time spent executing each line of code.

Directory Reports - Scan all the files in a directory and report on code efficiency, file                      

Differences, file dependencies, and code coverage.