Noise Cancellation In Heart Beat Signal using MATLAB Source Code

Noise Cancellation In Heart Beat Signal using MATLAB Source Code

Project report By Awad Zafar, Computer Engineer, UET Taxila.

ABSTRACT:
Different techniques for cancellation of noise in audio signals are being readily used now a day for different purposes. The basic part of this is a filter, which are of two basic types namely IIR and FIR filters.

In adaptive noise canceling, a measured signal d(n) contains two signals: – an unknown signal of interest v(n) – an interference signal u(n) The goal is to remove the interference signal from the measured signal by using a reference signal x(n) that is highly correlated with the interference signal. The example considered here is an application of adaptive filters to fetal electrocardiography, in which a maternal heartbeat signal is adaptively removed from a fetal heartbeat sensor signal

1. INTRODUCTION

For noise cancellation using matlab, we want to cancel out noise or extra frequencies from a signal. For achieving this purpose different types of filter are used which process a signal and filter out the undesired signals from the source signal leaving behind a noise-free signal.

Different types of the filter include low-pass; high-pass, band-pass and band-stop which can be designed either as FIR filters or IIR filters.

1.1 Problem Statement
In electronic-diagnoses of heartbeat, doctors find many noise factors added up with the original signal which hinders in the exact diagnoses of disease or even hides many symptoms

1.2 Project Objective

Aims of this project are:

1.2.1 To develop a method to nullify these noises without disturbing the actual signal.

1.2.2 Also to learn about filters, their uses and details about them.

1.2.3 To become familiar with MATLAB tools and functions.

1.3 Project Scope:
This project is divided into two parts; hardware and software development. The hardware device is consisting of filter circuit as the main element while the software is to build a MATLAB program which is to demonstrate the actual working of filter on software.

1.4 Project Methodology:
In this project, we formed a desired heartbeat signal by MATLAB commands and also generated a possible noise signal. Then we passed this signal through different filters i.e FIR and IIR and checked their outputs as MATLAB figures.

2. DESCRIPTION:
In this project, we used MATLAB tool to design filters and signals. Description of filters is given below.

2.1 FILTERS

Filters are used to pass desired frequencies and stop rest of it of a given system. Filters may be low-pass, high pass, band-pass or band-stop. They can be designed as FIR or IIR.

2.1.1 FIR FILTERS MATLAB NOISE CANCELLATION

Finite Impulse Response filters can be designed directly in the discrete domain. They are non-recursive systems. i.e. they have no feedback part. They are easy to design. Their group delay is linear.

2.1.2 IIR FILTERS

Infinite Impulse Response filters cannot be designed in discrete time domain they are actually designed in the continuous time and then converted to discrete time. They are recursive systems and hard to implement. They have nonlinear group delay.

2.1.3 ADAPTIVE Filter MATLAB NOISE CANCELLATION

Conventional digital signal processing techniques do exist to extract a desired biomedical signal from a mixed signal which is usually contaminated by unwanted noises. Adaptive filters are used for non-stationary signals where a sample-by-sample adaptation process is required. Applications of adaptive filtering include multi-channel noise reduction, radar or sonar signal processing, channel equalization for cellular mobile phones, echo cancellation and low delay speech coding. This section discussed the concept of the adaptive filtering, adaptive algorithm and the Recursive Least Square (RLS) algorithm.

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2.2 MATLAB

Matlab is an effective tool build by Matrix Laboratory. It is being used to develop programs and prototype of different engineering projects. It is effectively used in signal processing too.CONCLUSION

We concluded that the Adapter filters are more effective than FIR and IIR filters as their responses can be seen in the diagram