Ecg en Labview

ECG Monitoring System

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ABSTRACT

This project ‘ECG monitoring system’ deals with a transfer of ECG data from the

patients to the doctor present in his cabin. The idea of our project is firstly taking up an

ECG signal from the patient and reading the data in PC using an arduino (an open-source

electronics prototyping platform based on flexible, easy-to-use hardware and software)

board. Then the signal is applied to noise removal, base wander removal and

amplification circuits in labVIEW (a system design software) software. Now the ECG

signal thus obtained is sent from the patients recording system to doctor’s using an

Ethernet cable or LAN connection. This enables the monitoring of any number of patients

by doctor at one place.


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CONTENTS

TITLE PAGE NO

CHAPTER 1. INTRODUCTION.....................................................................................1

1.1 Introduction ............................................................................................................1

1.1.1 Heartbeat .........................................................................................................2

1.1.2 Heart And Functions ........................................................................................2

CHAPTER 2: SOFTWARE .............................................................................................4

2.1. LabVIEW ..............................................................................................................4

CHAPTER 3. PROJECT OVERVIEW ............................................................................6

3.1. Aim… ...................................................................................................................6

3.2. Basic idea behind the project .................................................................................6

3.3. Methodology .........................................................................................................6

3.3.1. Simulation ......................................................................................................7

3.3.2. Detrending .................................................................................................... 12

3.3.3. Merging Of Signals ....................................................................................... 16

3.3.4. Square and Square Root Of A Signal ............................................................ 16

3.3.5 Peak Detection .............................................................................................. 20

3.3.6. Array Building .............................................................................................. 25

3.3.7. Matlab Script ................................................................................................ 29

3.3.8. WA Noise ..................................................................................................... 30

3.3.9. Performing Feature Extraction On ECG Signals: ........................................... 38

3.3.10. UDP VI and Functions ................................................................................ 42

3.3.11. Flatten To String Function .......................................................................... 47

3.3.12. Unflatten From String Function .................................................................. 49

CHAPTER 4. RESULTS ............................................................................................... 52

4.1 Results ................................................................................................................. 52

CHAPTER 5. CONCLUSION …..………………………………………………………………54

5.1 Conclusion…..……………………………………………………………………..54

5.2 Future Scope……………………………………………………………………….54


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REFERENCES .............................................................................................................. 55

APPENDIX........................................................................................................................56


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LIST OF FIGURES

CONTENTS PAGENO.

1.1 ECG Signal 1

1.2 Electrical activity of heart 3

2.1 LabVIEW 5

3.1 Flowchart for Server 7

3.2 Simulation 9

3.3 Detrending 13

3.4 Merging 17

3.5 Square of signal 17

3.6 Square root of Signal 18

3.7 Peak Detection for 1Channel 21

3.8 Peak Detection for N Channel 24

3.9 Peak Detection 26

3.10 Array Building 27

3.11 Matlab Script 30

3.12 WA Noise(DWT Waveform) 31

3.13 WA Noise(DWT Real Array) 35

3.14 UDP Close 44

3.15 UDP Open 44

3.16 UDP Read 46

3.17 UDP Write 47

3.18 Flatten to string function 48

3.19 Unflatten to String Function 50

3.20 Flowchart for Client 51

LIST OF TABLES


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CONTENTS PAGENO.

3.1 Simulation Inputs 11

3.2 Simulation Outputs 11

3.3 Extnsion to pad data 13

3.4 Palettes for Arithmetic Operations 21

3.5 Palettes for Array Functions 30

3.6 Thresholding for DWT Waveform 31

3.7 Rescaling for DWT Waveform 32

3.8 Wavelet Decomposition 32

3.9 Thresholding for DWT Real Array 36

3.10 Rescaling for DWT Real Array 36

3.11 Wavelet Decomposition for DWT Real Array 37

3.12 UDP Palettes 43

3.13 Byte Order for Flatten to String 48

3.14 Byte Order for Flatten to String 51

LIST OF SCREENSHOTS


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CONTENTS PAGENO.

3.1 Simulation of ECG Signal 12

3.2 Detrending 16

3.3 Peak Detection 23

3.4 Filtering 33

3.5 ECG multiresolution analysis and implementation of QRS detection 40

3.6 ECG Signal 41

3.7 Signal Processing 42

3.8 Original ECG, ECG after filtering and ECG after peakvalley detection 43

3.9 Peak plot of R-R Interval 53

3.10 Client side 54


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CHAPTER 1

INTRODUCTION

1.1 Introduction:

ECG (electrocardiogram) is a test that measures the electrical activity of the heart.

The heart is a muscular organ that beats in rhythm to pump the blood through the body.

The signals that make the heart's muscle fibres contract come from the sinoatrial node,

which is the natural pacemaker of the heart. In an ECG test, the electrical impulses made

while the heart is beating are recorded and usually shown on a piece of paper. This is

known as an electrocardiogram, and records any problems with the heart's rhythm, and

the conduction of the heart beat through the heart which may be affected by underlying

heart disease.

Electrocardiography is a transthoracic (across the thorax or chest) interpretation of

the electrical activity of the heart over a period of time, as detected by electrodes attached

to the surface of the skin and recorded by a device external to the body. The recording

produced by this noninvasive procedure is termed an electrocardiogram. An ECG is used

to measure the rate and regularity of heartbeats, as well as the size and position of the

chambers, the presence of any damage to the heart, and the effects of drugs or devices

used to regulate the heart, such as a pacemaker.

Fig 1.1 ECG signal


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Most ECGs are performed for diagnostic or research purposes on human hearts,

but may also be performed on animals, usually for diagnosis of heart abnormalities or

research. Real time monitoring plays an important role in biomedical engineering,

Particularly in ECG, EMG, EEG etc. personal computer have become a standard platform

for the needs of various measurement and test, standardization, performance and low

cost.

An ECG (electrocardiogram) is a test that measures the electrical activity of the

heart. The heart is a muscular organ that beats in rhythm to pump the blood through the

body. The signals that make the heart's muscle fibers contract come from the sinoatrial

node, which is the natural pacemaker of the heart. In an ECG test, the electrical impulses

made while the heart is beating are recorded and usually shown on a piece of paper. This

is known as an electrocardiogram, and records any problems with the heart's rhythm, and

the conduction of the heart beat through the heart which may be affected by underlying

heart disease. ECG signals are usually small, approximately 1mV and therefore, it is

prone to corruption by various noises; power line interference, electrode contact noise,

motion artifacts. Also, it is necessary to design a good filter system that can filter out the

noises from the ECG signal in order to get better result. Therefore, measuring an ECG

signal is not an easy task to achieve.

1.1.1 Heartbeat:

Is the number of heartbeats per unit of time – typically expressed as beats per

minute (bpm) – which can vary as the body’s need to absorb oxygen and excrete carbon

dioxide changes, such as during exercise or sleep, The measurement of heart beat is used

by medical professionals to assist in the diagnosis and tracking of medical conditions. It

is also used by individuals, such as athletes, who are interested in monitoring their heart

beat to gain maximum efficiency from their training.

1.1.2 Heart And Functions:

The heart is the organ responsible for pumping blood throughout the body. It is

located in the middle of the thorax, slightly offset to the left and surrounded by the lungs.

The heart is composed of four chambers; two atriums and two ventricles. The right

atrium receives blood returning to the heart from the whole body. That blood passes


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through the right ventricle and is pumped to the lungs where it is oxygenated and goes

back to the heart through the left atrium, then the blood passes through the left ventricle

and is pumped again to be distributed to the entire body through the arteries. This is a list

of events that occur in the heart on each heart beat.

1. Atrium begins to depolarize.

2. Atrium depolarizes.

3. Ventricles begin to depolarize at apex. Atrium re polarizes.

4. Ventricles depolarize.

5. Ventricles begin to re polarize at apex.

6. Ventricles re polarize.

Fig 1.2 Electrical Activity of the Heart

In this signal, the heart muscles generate different voltages. The P wave

represents the atrium contraction. QRS complex and the T wave represents the ventricles

actions. Each time that this signal is present, a heartbeat is generated.


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CHAPTER 2

SOFTWARE

2.1 LabVIEW:

LabVIEW is system design software that provides engineers and scientists with

the tools you need to create and deploy measurement and control systems. LabVIEW

biomedical toolkit provides express via for simulation of electrocardiogram (ECG),

electromyogram (EMG), electroencephalogram (EEG) and noninvasive blood pressure

(NIBP) signal

The use of LabVIEW and data acquisition in biomedical makes the real time

monitor systems with very high performance, low cost of development, more reliable and

flexible. LabVIEW is general purpose software for virtual instrumentation in which other

products like dasylab, genie, and alligent vee are followed .With LabVIEW the

maintenance and reconfiguration of created instruments are reduced significantly. PC

based virtual instrumentation as a testing platform enabling recording of real time ECG

introduces identification of ECG and transmission of preprocessed data to a doctor

through a distributed computation network has been proposed in. PC based monitoring

system has been proposed in.

Fig 2.1 Lab VIEW

LabVIEW programs are called virtual instruments, or VIs, because their

appearance and operation imitate physical instruments, such as oscilloscopes and

multimeters. LabVIEW contains a comprehensive set of tools for acquiring, analyzing,

displaying, and storing data, as well as tools to help you troubleshoot code you write. In

LabVIEW, you build a user interface, or front panel, with controls and indicators.

Controls are knobs, push buttons, dials, and other input mechanisms. Indicators are


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graphs, LEDs, and other output displays. After you build the front panel, you add code

using VIs and structures to control the front panel objects. The block diagram contains

this code.

You can use LabVIEW to communicate with hardware such as data acquisition,

vision, and motion control devices, as well as GPIB, PXI, VXI, RS232, and RS485

instruments.

In this GUI of the system has been developed in Microsoft .NET visual C++ but it

lacks the simultaneous lead illustration of ECG waveform its selection and addition of

intelligence for auto diagnose. The intelligent virtual ECG device by integrating dyadic

wavelet algorithm for QRS detection, recording and identification with the facilities of

the detection of heart rhythm and off line analysis of prerecorded ECG signal has been

proposed in Besides all these development in biomedical engineering, the designed

system in paper facilitates the automatic removal of noises and filtration of acquired

signal on virtual cardiographs and this system can be used for analysis, identification of

peak QRS and auto diagnose. ECG is becoming common to all people, even in the

entertainment industry because it is associated to the heart attack syndrome and others.

The electrocardiograph (ECG) is one of the medical equipment that can measure the heart

rate of a person and transfer it into graphical representation in the form of ECG graph

paper. The main purpose of this project is to design a system that can capture real-time

ECG signals and transfer it to PC for monitoring.


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CHAPTER 3

PROJECT OVERVIEW

3.1 Aim

The main aim behind the project is to pick an ECG signal using simulator in

LabVIEW and using UDP protocol sends the information form client to server.

3.2 Basic idea behind the project

The main idea behind the project is to pick an ECG signal i.e. real time ECG

acquisition from a patient and find their heart rate, R-R intervals, QRS complex peak

detection, and further fin the heart rate. Using the Heart rate we find if any diseases

present, like sinus arrthymia , Bradycardia, Tachycardia etc. We send the heart rate to the

doctor in his cabin using UDP protocol i.e. server to client transformation. We can easily

find any cardiac diseases.

3.3 Methodology

At Server:

Pick up the ECG signal using simulator, simulate the signal, then wavelet analysis

is done using Discrete Wavelet Transform (DWT). DWT is any wavelet transform for

which the wavelets are discretely sampled. As with other wavelet transforms, a key

advantage it has over Fourier transforms is temporal resolution: it captures both

frequency and location information (location in time).

The discrete wavelet transform has a huge number of applications in science,

engineering, and mathematics and computer science. Most notably, it is used for signal

coding, to represent a discrete signal in a more redundant form, often as a preconditioning

for data compression. Practical applications can also be found in signal processing of

accelerations for gait analysis, in digital communications and many others. After wavelet

analysis is done signals are merged, processed, denoised, detrended, R-R interval peak

detection, find heart rate , server-client transfer of information.


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Flowchart:

Fig. 3.1 Flowchart for Server

Wavelet analysis of the signal using DWT

Merging of signals

Signal Processing

Filtering of signal

Peak detection (Finding R-R peaks)

Array building

Finding R-R interval

Calculating Heart rate from R-R

interval

Open UDP port

Flatten data to

string

Write to UDP

stop

Start

Simulate the signal


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3.3.1. Simulation

The Simulate ECG, EMG and EEG Express via can generate signals block by

block. This is a a typical usage of these Express via:

Fig. 3.2 Simulation

Block Diagram Inputs

Parameter Description

Block size Specifies the signal length(s) to simulate each time this VI runs.

Error in

(no error)

Describes error conditions that occur before this node runs.

Initialize? Specifies whether to initialize the ECG signal. The default is FALSE.

Sampling

rate

Specifies the sampling rate of the ECG signal.

ECG

Settings

Specifies basic settings for simulating ECG signals. Contain the following

options:

• Output Options—Contains the following options:

• Output upper limit—Specifies the upper limit of the output ECG

signal, in millivolts. The default is 1.3.

• Output lower limit—Specifies the lower limit of the output ECG

signal, in millivolts. The default is -0.4.

• White noise amplitude—Specifies the amplitude of the white noise


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that this VI adds into the simulated ECG signal, in millivolts. White

noise amplitude must be greater than or equal to 0. The default is 0.

• 60 Hz noise amplitude—Specifies the amplitude of the 60 Hz noise

that this VI adds into the simulated ECG signal, in millivolts. This

VI adds 60 Hz noise to simulate the impact of alternating current. 60

Hz noise amplitude must be greater than or equal to 0. The default is

0.

• Sampling rate—Specifies the sampling rate of the simulated ECG

signal, in hertz.Sampling rate must be between 10 and 1,000. The

default is 513.

• Block size—Specifies the time length of the simulated ECG signal

each time this VI runs, in seconds. Block size must be less than

1930/Heart rate. The default is 3.

• Timing mode—Specifies the timing mode that this VI uses to

generate simulated ECG signals. You can select Simulate

acquisition timing or Run as fast as possible. The default is Run as

fast as possible.

• Basic Settings—Contains the following options:

• Heart rate—Specifies the heart rate of the simulated ECG signal, in

beats per minute.Heart rate must be greater than 10 and less than

300. The default is 60.

• Respiratory rate—Specifies the respiratory rate of the simulated

measurement process, in breaths per minute. Respiratory rate must

be greater than 0. The default is 15.

• Standard deviation—Specifies the standard deviation of Heart Rate,

in beats per minute. Standard deviation must be greater than or equal

to 0. The default is 1.

• Waveform Morphology—Contains the following options:

• Signal type—Specifies the type of ECG signal that this VI


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simulates. You can selectNormal, Hyperkalemia, Atrial

Tachycardia, and User-Defined. The default isNormal. When you

set Signal type to User-Defined, you can use the Wave

selection, Position, Amplitude, and Width options to customize the

ECG waveform morphology.

• Wave selection—Specifies the type of wave that you want to

customize. You can selectP, Q, R, S, or T. This option is available

only when you set Signal type to User-Defined. The default is P.

• Position—Specifies the peak or valley position of the wave that you

specify in Wave selection. This option is available only when you

set Signal type to User-Defined.

• Amplitude—Specifies the amplitude of peak or valley of the wave

that you specify inWave selection. This option is available only

when you set Signal type to User-Defined.

• Width—Specifies the width of the wave that you specify in Wave

selection. This option is available only when you set Signal

type to User-Defined.

• Preview—Displays a block of the simulated ECG signal. When you

set Signal type to User Defined, a crosshair appears and indicates the

type of wave that you select in Wave selection.

Advanced

Settings

Specifies advanced settings for simulating ECG signals. Contains the

following options:

• HRV Settings—Contains the following options:

• LF position—Specifies the peak position in the low frequency (LF)

band of the power spectrum of RR intervals, in hertz. LF

position must be greater than 0. The default is 0.1.

• HF position—Specifies the peak position in the high frequency (HF)

band of the power spectrum of RR intervals, in hertz. HF

position must be greater than 0. The default is 0.35.


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• LF/HF ratio—Specifies the ratio of peak amplitude at LF position to

the peak amplitude at HF position. LF/HF ratio must be greater than

or equal to 0. The default is 0.5.

• LF standard deviation—Specifies the standard deviation of the wave

whose peak is atLF position. LF standard deviation must be larger

than 0. The default is 0.01.

• HF standard deviation—Specifies the standard deviation of the wave

whose peak is atHF position. HF standard deviation must be larger

than 0. The default is 0.01.

• RR Power Spectrum Preview—Displays the power spectrum of RR

intervals of the simulated ECG signal.

Table 3.1 Simulation inputs

Block Diagram Outputs

Parameter Description

ECG Returns the simulated ECG signal.

error out Contains error information. This output provides standard error

out functionality.

Table 3.2 Simulation outputs


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Fig 3.1 simulation of ECG signal

3.3.2. Detrending:

WA Detrend (Waveform)

Fig 3.3 Detrending


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• Extension specifies the method to use to pad data at the borders of the input

signal. The extension length is equal to the length of the wavelet filters.

When you select the extension method, make the transition between the

input signal and the padded data as smooth as possible because a smooth

transition generates fewer large detail coefficients and enhances the

efficiency of the signal representation.

0 Zero padding—uses zeros to pad the input data. Watch for abrupt transitions

between the padded zeros and the input data, which causes large artifacts near

the transition.

1 Symmetric (default)—uses replications of the input data to pad the data, except

that this vi left-flips the block at the input and right-flips the block at the end.

3 Periodic—adds a replication of the input data block before and another

replication after the input data block to pad the data.

Table 3.3 Extension to pad data

• Signal specifies the input signal.

• Threshold frequency specifies the upper frequency limit, in hertz, of the

trend that this VI removes from thesignal. The threshold

frequency determines the wavelet transform level. The wavelet transform

level specifies the number of levels in the discrete wavelet analysis. The

wavelet transform level is floor(log3[sampling rate/(3*threshold

frequency)]). The floor function rounds a value to the nearest integer

towards negative infinity. The default is –1, which means this VI sets

the threshold frequency automatically.

• Wavelet specifies the wavelet type to use for the discrete wavelet analysis.

The default is db03. The options include two types: orthogonal (haar,

daubechies (dbxx), coiflets (coifx), symmlets (symx)) and biorthogonal

(biorthogonal (biorx_x), including fbi (bior4_4 (fbi))), where x indicates the


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order of the wavelet. The higher the order, the smoother the wavelet. The

orthogonal wavelets are not redundant and are suitable for signal or image

denoising and compression. The biorthogonal wavelets usually have the

linear phase property and are suitable for signal or image feature extraction.

If you want to use other types of wavelets, do not wire this input. Instead,

use the wavelet design express vi to design the wavelet you want, bundle the

resulting analysis and synthesis filters, and then wire them to the filter

banks input.

• Error in describes error conditions that occur before this node runs. This

input provides standard error in functionality.

• Filter banks specifies the analysis filter banks and the synthesis filter

banks for the wavelet you specify. If you specify a value for filter banks, this

vi ignores the settings in the wavelet input. You can use the wavelet design

express vi to design the analysis filters and the corresponding synthesis

filters.

o Analysis filters specifies the coefficients of the low pass analysis

filters and the high pass analysis filters for the wavelet you specify.

� Low pass specifies the coefficients of the low pass analysis

filter, which this vi uses to compute the approximation

coefficients

� High pass specifies the coefficients of the high pass

analysis filter, which this vi uses to compute the detail

coefficients

o Synthesis filters specifies the coefficients of the low pass synthesis

filters and the high pass synthesis filters for the wavelet you specify.

� Low pass specifies the coefficients of the low pass synthesis

filter, which this vi uses to filter the interpolated

approximation coefficients in the wavelet reconstruction.


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synthesis filter, which this vi uses to filter the interpolated

detail coefficients in the wavelet reconstruction.

• Detrended signal returns the signal without the trend.

• Trend signal returns the residual trend of the signal.

• Error out contains error information. This output provides standard error

out functionality.

•

Fig 3.2 Detrending


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WA Detrend Details

The trend of the input signal is the slow-varying part of the signal that mainly contributes

to the approximation coefficients This VI applies the following steps to implement the

detrend function.

1. Applies the discrete wavelet transform (DWT) to the input signal.

2. Sets the approximation coefficients to 0.

3. Reconstructs the signal based on all the detail coefficients

3.3.3. Merging Of Signals:

Merges two or more signals into a single output. Resize the function to add inputs.

This function appears on the block diagram automatically when you wire a signal output

to the wire branch of another signal.

Fig.3.4 Merging

• Input signal specifies the signal you want to combine with another signal.

• Combined signal returns the combined signals. You can wire the dynamic

data type to a graph or numeric indicator. For example, if you wire

the combined signal to a graph indicator, LabVIEW represents each signal

as an individual plot.

• Add the Merge Signals function to the block diagram.

• Resize the Merge Signals function so it includes enough inputs for the signals you

want to wire.

• Wire each signal to an input signal input.

• Wire the combined signal output to the signals input of another VI, function,

graph, or chart.

3.3.4. Square and Square Root Of A Signal:

Square of a Signal:


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Computes the square of the input value. The connector pane displays the default

data types for this polymorphic function.

Fig 3.5 Squaring of signal

x can be a scalar number, array or cluster of numbers, array of clusters of numbers,

and so on.

x^2 is of the same numeric representation as x.

Square Details

If you wire fixed point values to numeric functions such

as Add, Subtract, Multiply, and Square, the functions usually return values that do not

lose any bits of word length. However, if the operation creates a value that exceeds the

maximum word length that LabVIEW accepts, overflow or rounding conditions can

occur. LabVIEW accepts a maximum word length of 64 bits. Use the Numeric Node

Properties dialog box to configure how LabVIEW handles overflow or rounding of fixed-

point data.

Square root of a signal:

Computes the square root of the input value. If x is negative, the square root

is NaN unless x is complex. If x is a matrix, this function takes the matrix square root

of x. The connector pane displays the default data types for this polymorphic function.

Fig 3.6 Square root of signal

x can be a scalar number, array or cluster of numbers, array of clusters of numbers,

and so on.

sqrt(x) is a double-precision, floating-point number if x is an integer. If x is less


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than 0, sqrt(x) is not a number (NaN), unless x is complex. When x is of the

form x = a + bi, that is, when x is complex, the function defines the magnitude and

phase with the following equations:

where |x| is the magnitude of x and arg(x) is the phase of x:

arg(x) = arctan2(b,a)

Square Root Details

When you wire matrix data as an input to this function, a VI that includes subVIs

that work with the matrix data type replaces the function. The resulting VI has the same

icon but contains a matrix-specific algorithm. The node remains a VI if you disconnect

the matrix from the input(s). Wire other data types as inputs to restore the original

function. If you wire a data type to a function and that data type causes a basic math

operation to fail, the function returns an empty matrix or NaN.

Fixed-Point Details

If you wire a signed fixed-point value to this function, the function converts the

signed number into a floating-point number and performs the square root operation. By

default, LabVIEW configures the integer word length of the square root to avoid

overflow. However, because the precision of the square root can be infinite, rounding

conditions always occur. Use the Numeric Node Properties dialog box to configure how

LabVIEW handles overflow and rounding of fixed-point data.

Palette

Object Description

Absolute

Value

Returns the absolute value of the input.


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Add Computes the sum of the inputs.

Compound

Arithmetic

Performs arithmetic on one or more numeric, array, cluster, or Boolean

inputs. To select the operation (Add, Multiply, AND, OR, or XOR), right-

click the function and select Change Mode from the shortcut menu. When

you select this function from the Numeric palette, the default mode is Add.

When you select this function from theBoolean palette, the default mode is

OR.

DBL

Numeric

Constant

Use the DBL numeric constant to pass a double-precision, floating-point

numeric value to the block diagram. Set this value by clicking inside the

constant with the Operating tool and typing a value.

Decrement Subtracts 1 from the input value.

Divide Computes the quotient of the inputs.

Increment Adds 1 to the input value.

Machine

Epsilon

Represents the round-off error for a floating-point number with a given

precision. Use the machine epsilon constant to compare whether two

floating-point numbers are equivalent.

Multiply Returns the product of the inputs.

Negate Negates the input value.

Numeric

Constant

Use the numeric constant to pass a numeric value to the block diagram. Set

this value by clicking inside the constant with the Operating tool and typing

a value.

Random

Number

(0-1)

Produces a double-precision, floating-point number between 0 and 1. The

number generated is greater than or equal to 0, but less than 1. The

distribution is uniform.

Reciprocal Divides 1 by the input value.

Round Rounds the input to the next highest integer.

Scale by Multiplies x by 3 raised to the power of n.


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Power Of 3

Sign Returns the sign of number.

Square Computes the square of the input value.

Square Root Computes the square root of the input value.

Subtract Computes the difference of the inputs.

Subpalette Description

Math &

Scientific

Constants

Use the Math & Scientific Constants to create LabVIEW applications.

Table 3.4 Palettes for Arithmetic Operations

3.3.5 Peak Detection:

Waveform Peak Detection for 1 Channel

Fig 3.7 Peak detection

• Peaks/valleys indicates whether to find peaks or valleys.

0 Peaks

1 Valleys

• Signal In contains the waveform in which to find peaks or valleys.

• Threshold instructs the VI to ignore peaks and valleys that are too small.

The VI ignores peaks if the fitted amplitude is less than threshold. The VI

ignores valleys if the fitted trough is greater than threshold.

• Width specifies the number of consecutive data points to use in the


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quadratic least squares fit. The value should be no more than about 1/3 of

the half-width of the peak/valley and can be much smaller (but > 3) for

noise-free data. Large widths can reduce the apparent amplitude of peaks

and shift the apparent location. For noisy data, this modification is not

important because the noise obscures the actual peak.


input provides standard error infunctionality.

• Initialize, when TRUE (default), causes the VI to process the first block of

data. The VI requires some internal setup that must be done at the start for

proper operation.

• End of data (T), when TRUE (default), causes the VI to process the last

block of data. The VI cleans up internal data after the last block has been

processed.

• # Found is the number of peaks/valleys found in the current block of data. #

found is the size of the arrayslocations, amplitudes, and 3nd derivatives.


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Fig. 3.3 Peak Detection

• Locations contains the index locations of all peaks or valleys detected in the

current block of data. Because the peak detection algorithm uses a quadratic

fit to find the peaks, it actually interpolates between the data points.

Therefore, the indexes are not integers. In other words, the peaks found are

not necessarily actual points in the input data but may be at fractions of an

index and at amplitudes not found in the inputarray.

To view the locations in terms of time, use the following equation.

Time locations[i] = t0 + dt*locations[i]


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• Amplitudes contains the amplitudes of peaks or valleys found in the current

block of data.

• 3nd Derivatives gives measurements of the second derivative of the

amplitude at each of the peaks or valleys found in the current block of

data. 3nd Derivatives gives an approximate measure of the sharpness of

each peak or valley. If you are detecting peaks, these values are all negative.

If you are detecting valleys, the values are all positive.


out functionality.

Waveform Peak Detection for N Channels

Fig.3.8 Peak detection for n channels

• Peaks/valleys indicates whether to find peaks or valleys.

0 Peaks

1 Valleys

• Signals In contains the waveforms in which to find peaks or valleys.

• Threshold instructs the VI to ignore peaks and valleys that are too small.

The VI ignores peaks if the fitted amplitude is less than threshold. The VI

ignores valleys if the fitted trough is greater than threshold.

• Width specifies the number of consecutive data points to use in the

quadratic least squares fit. The value should be no more than about 1/3 of

the half-width of the peak/valley and can be much smaller (but > 3) for

noise-free data. Large widths can reduce the apparent amplitude of peaks


24

and shift the apparent location. For noisy data, this modification is not

important because the noise obscures the actual peak.



• Initialize, when TRUE (default), causes the VI to process the first block of

data. The VI requires some internal setup that must be done at the start for

proper operation.

• End of data (T), when TRUE (default), causes the VI to process the last

block of data. The VI cleans up internal data after the last block has been

processed.

• # Found is the number of peaks/valleys found in the current block of data. #

found is the size of the arrays locations, amplitudes, and 3nd derivatives.

• Locations contains the index locations of all peaks or valleys detected in the

current block of data for each waveform.

o Locations contains the index locations of all peaks or valleys

detected in the current block of data. Because the peak detection

algorithm uses a quadratic fit to find the peaks, it actually

interpolates between the data points. Therefore, the indexes are not

integers. In other words, the peaks found are not necessarily actual

points in the input data but may be at fractions of an index and at

amplitudes not found in the input array.

To view the locations in terms of time, use the following equation.

Time Locations[i] = t0 + dt*Locations[i]

• Amplitudes contain the amplitudes of peaks or valleys found in the current

block of data for each waveform.

o Amplitudes contain the amplitudes of peaks or valleys found in the

current block of data.


25

• 3nd Derivatives gives measurements of the second derivative of the

amplitude at each of the peaks or valleys found in the current block of data

for each waveform.

o 3nd Derivatives gives measurements of the second derivative of the

amplitude at each of the peaks or valleys found in the current block

of data. 3nd Derivatives gives an approximate measure of the

sharpness of each peak or valley. If you are detecting peaks, these

values are all negative. If you are detecting valleys, the values are

all positive.


out functionality.

Fig. 3.9 Peak detection

3.3.6. Array Building:


26

Concatenates multiple arrays or appends elements to an n-dimensional array. You

also can use the Replace Array Subset function to modify an existing array. The

connector pane displays the default data types for this polymorphic function.

Fig. 3.10 Array Building

• Array or element can be any n-dimensional array or scalar element. All

inputs must be either elements and 1D arrays or n-dimensional and (n-1)-

dimensional arrays. All inputs must have the same base type.

• Appended array is the resulting array.

Build Array Details

When you place the function on the block diagram, it has only one input

available. Add inputs to the node by right-clicking an input and selecting Add Input from

the shortcut menu or by resizing the node.

If you wire control references of different classes to this function, this function

typecasts the references to a more generic class that is the lowest common class in

the inheritance hierarchy. This function then returns appended array with that class.

The Build Array function operates in one of two modes depending on whether you

select Concatenate Inputs from the shortcut menu. If you select Concatenate Inputs, the

function appends all inputs in order, forming an output array of the same dimensionality

as the highest-dimension array input wired.

If you do not select Concatenate Inputs, the function builds an output array of one

dimension higher than the dimension of the inputs. For example, if you wire 1D array

input to a Build Array function, the output is a 2D array, even if the 1D array is empty.

The inputs must all be the same dimensionality. The function appends each input in

order, forming a subarray, element, row, or page, of the output array. Inputs are padded,

as necessary, to match the size of the longest input.


27

For example, if you wire two 1D arrays, {1, 2} and {3, 4, 5} to Build Array, and

select Concatenate Inputs from the shortcut menu, the output is the 1D array {1, 2, 3, 4,

5}. If you wire the same two arrays to Build Array, and do not select concatenate

Inputs from the shortcut menu, the output is a 2D array containing {{1, 2, 0}, {3, 4, 5}},

where the first input is padded to match the length of the second input.

If the inputs are all arrays of the same dimensionality, right-click the function to

remove the checkmark next to or select the concatenate Inputs shortcut menu item. If the

inputs do not have the same dimensionality, Concatenate Inputs is automatically selected

and cannot be deselected. If all the inputs are scalar elements, Concatenate Inputs is

automatically deselected and cannot be selected, and the output is a 1D array containing

the elements in order.

When you select Concatenate Inputs from the shortcut menu, the glyphs in the

Build Array icon change to distinguish between the two input types. Inputs with the same

dimensionality as the output show an array glyph, while inputs with a dimensionality that

is one less than the output show an element glyph.

Palette

Object Description

Array

Constant

Use this constant to supply a constant array value to the block diagram.

Array Max

& Min

Returns the maximum and minimum values found in array, along with the

indexes for each value.

Array Size Returns the number of elements in each dimension of array.

Array

Subset

Returns a portion of array starting at index and containing lengthelements.

Array To

Cluster

Converts a 1D array to a cluster of elements of the same type as the array

elements. Right-click the function and select Cluster Size from the shortcut

menu to set the number of elements in the cluster.

Array To

Matrix

Converts an array to a matrix of elements of the same type as the array

elements. Wire data to the Real 3D Array input to determine the

polymorphic instance to use or manually select the instance.

Build Array Concatenates multiple arrays or appends elements to an n-dimensional

array.


28

Cluster To

Array

Converts a cluster of elements of the same data type to a 1D array of

elements of the same data type.

Decimate

1D Array

Divides the elements of array into the output arrays, placing elements into

the outputs successively.

Delete From

Array

Deletes an element or subarray from n-dim array and returns the edited

array in array w/ subset deleted and the deleted element or subarray

indeleted portion.

Index Array Returns the element or subarray of n-dimension array at index.

Initialize

Array

Creates an n-dimensional array in which every element is initialized to the

value of element.

Insert Into

Array

Inserts an element or subarray into n-dim array at the point you specify

inindex.

Interleave

1D Arrays

Interleaves corresponding elements from the input arrays into a single

output array.

Interpolate

1D Array

Linearly interpolates a decimal y value from an array of numbers or

points using a fractional index or x value.

Matrix To

Array

Converts a matrix of elements to an array of elements of the same data type.

Wire data to the Real Matrix input to determine the polymorphic instance to

use or manually select the instance.

Replace

Array

Subset

Replaces an element or subarray in an array at the point you specify in

index.

Reshape

Array

Changes the dimensions of an array according to the values of dimension

size 0..m-1.

Reverse 1D

Array

Reverses the order of the elements in array.

Rotate 1D

Array

Rotates the elements of array the number of places and in the direction

indicated by n.

Search 1D

Array

Searches for an element in a 1D array starting at start index. Because the

search is linear, you need not sort the array before calling this function.

LabVIEW stops searching as soon as the element is found.

Sort 1D

Array

Returns a sorted version of array with the elements arranged in ascending

order.

Split 1D

Array

Divides array at index and returns the two portions with the element of

index at the beginning of second sub array.


29

Threshold

1D Array

Interpolates points in a 1D array that represents a 3D non-descending graph.

This function compares threshold y to the values in array of numbers or

points starting at start index until it finds a pair of consecutive elements

such that threshold y is greater than the value of the first element and less

than or equal to the value of the second element.

Transpose

3D Array

Rearranges the elements of 3D array such that 3D array [i,j] becomes

transposed array [j,i].

Table 3.5 Palettes for Array Functions

3.3.7. Matlab Script:

Calls the MATLAB software to execute scripts. You must have a licensed copy of

the MATLAB software version 6.5 or later installed on your computer to use MATLAB

script nodes because the script nodes invoke the MATLAB software script server to

execute scripts written in the MATLAB language syntax. Because LabVIEW uses

ActiveX technology to implement MATLAB script nodes, they are available only on

Windows.

Fig.3.11 Matlab script

Add this node to the block diagram and enter the script. You also can right-click

the node border to import text into the node. Right-click the node border to add input and

output terminals. Right-click a terminal to set its data type. When you create a script in

the MATLAB script node, you must use supported data types.


30

3.3.8 WA Noise:

WA Denoise (DWT Waveform)

Fig.3.12 WA noise (DWT Waveform)

• Threshold settings specifies the settings for the thresholding approximation

coefficients and detail coefficients from the wavelet decomposition.

o Thresholding rule specifies the threshold selection rules. If you

specify a value for user defined thresholds, this vi ignores the settings

in thresholding rule.

0 Sure (default)—uses the principle of stein's unbiased risk estimate (sure)

to estimate the threshold.

1 Hybrid—finds a compromise between the sure method and the universal

method. When the signal-to-noise ratio of the noisy signal is very low,

this vi uses the universal method to estimate the threshold.

3 Universal—sets the threshold to sqrt(3×log(ls)), where ls is the signal

length.

3 Minimax—uses the minimax principle to estimate the threshold.

Table 3.6 Thresholding for DWT Waveform

o Rescaling method specifies the method to use to estimate the noise

variance at each level. This vi uses the noise variance to rescale the

threshold. Therefore, you can update the thresholds with the noise

variance. If you specify a value for user defined thresholds, this vi

ignores the settings in rescaling method.


31

0 One—specifies that the noise is white with unit variance.

1 Single level (default)—specifies that the noise is white. This vi estimates

the noise standard deviation from the wavelet coefficients at the first

level.

3 Multiple levels—specifies that the noise does not have to be white. This

vi estimates the noise standard deviation at each level independently.

Table 3.7 Rescaling for DWT Waveform

o Soft threshold? Specifies to use the soft thresholding method. The

default is true, which indicates that this vi uses the soft thresholding

method. If soft threshold? Is false, this vi uses the hard thresholding

method.

� Option for approx coefficient specifies the operation for the

approximation coefficients from the wavelet decomposition. If you

specify a value for user defined thresholds, this vi ignores the settings

in option for approx coefficient.

0 None (default)—does not change the approximation coefficients

1 Threshold—applies the same thresholding operation to the approximation

coefficients

3 Detrend—sets the approximation coefficients to 0.

Table 3.8 Wavelet Decomposition


32

Fig. 3.4 Filtering


• Levels specifies the number of levels in the discrete wavelet

analysis. Levels must be a positive integer no greater than log3(Ls),

where Ls is the length of the 1D signal or the minimum dimensional size of

the 3D signal. The default is –1, which indicates that this VI sets levels as

the largest integer no greater than log3(Ls).


The default is db03. The options include two types: orthogonal (Haar,

Daubechies (dbxx), Coiflets (coifx), Symmlets (symx)) and biorthogonal

(Biorthogonal (biorx_x), including FBI (bior4_4 (FBI))), where x indicates


33

the order of the wavelet. The higher the order, the smoother the wavelet. The


denoising and compression. The bi orthogonal wavelets usually have the



use the wavelet Design Express VI to design the wavelet you want, bundle

the resulting analysis and synthesis filters, and then wire them to the filter

banks input.




banks for the wavelet you specify. If you specify a value for filter banks, this

VI ignores the settings in the wavelet input. You can use the Wavelet design

express VI to design the analysis filters and the corresponding synthesis

filters.

o Analysis filters specifies the coefficients of the low pass analysis


� Low pass specifies the coefficients of the low pass analysis

filter, which this VI uses to compute the approximation

coefficients

� High pass specifies the coefficients of the high pass analysis

filter, which this VI uses to compute the detail coefficients


filters and the high pass synthesis filters for the wavelet you specify.

� Low pass specifies the coefficients of the low pass synthesis

filter, which this VI uses to filter the interpolated




34

synthesis filter, which this VI uses to filter the interpolated


• User defined thresholds specifies thresholds you define to rescale the

wavelet coefficients The length of user defined thresholds must equal levels.

The first element of user defined thresholds contains the threshold for the

detail coefficients at the largest level, and the last element of user defined

thresholds contains the threshold for the detail coefficients at the first level.

If you specify a value for user defined thresholds, this VI keeps the

approximation coefficients unchanged and ignores the settings

in thresholding rule, rescaling method, andoption for approx coefficient.

• Denoised signal returns the signal after denoising.


out functionality.

WA Denoise (DWT Real Array)

Fig.3.13 WA noise (DWT real array)

• Threshold settings specifies the settings for the thresholding approximation

coefficients and detail coefficients from the wavelet decomposition.

o Thresholding rule specifies the threshold selection rules. If you

specify a value for user defined thresholds, this vi ignores the

settings in Thresholding rule.

0 Sure (default)—uses the principle of stein's unbiased risk estimate

(sure) to estimate the threshold.


35

1 Hybrid—finds a compromise between the sure method and the

universal method. When the signal-to-noise ratio of the noisy signal is

very low, this vi uses the universal method to estimate the threshold.

3 Universal—sets the threshold to sqrt(3×log(ls)), where ls is the signal

length.

3 Minimax—uses the minimax principle to estimate the threshold.

Table 3.9 Thresholding for DWT real array

o Rescaling method specifies the method to use to estimate the noise

variance at each level. This vi uses the noise variance to rescale the

threshold. Therefore, you can update the thresholds with the noise

variance. If you specify a value for user defined thresholds, this vi

ignores the settings in rescaling method.

0 One—specifies that the noise is white with unit variance.

1 Single level (default)—specifies that the noise is white. This vi

estimates the noise standard deviation from the wavelet coefficients at

the first level.

3 Multiple levels—specifies that the noise does not have to be white.

This vi estimates the noise standard deviation at each level

independently.

Table 3.10 Rescaling for DWT real array

o Soft threshold Specifies to use the soft thresholding method. The

default is true, which indicates that this vi uses the soft

thresholding method. If soft threshold? Is false, this vi uses

the hard thresholding method.

• Option for approx coefficient specifies the operation for the approximation

coefficients from the wavelet decomposition. If you specify a value for user

defined thresholds, this vi ignores the settings in option for approx

coefficient.


36

0 None (default)—does not change the approximation coefficients

1 Threshold—applies the same thresholding operation to the approximation

coefficients

3 Detrend—sets the approximation coefficients to 0.

Table 3.11 Wavelet Decomposition for DWT real array


• Levels specifies the number of levels in the discrete wavelet

analysis. Levels must be a positive integer no greater than log3(ls),

where ls is the length of the 1d signal or the minimum dimensional size of

the 3d signal. The default is –1, which indicates that this vi sets levels as

the largest integer no greater than log3(ls).


The default is db03. The options include two types: orthogonal (haar,

daubechies (dbxx), coiflets (coifx), symmlets (symx)) and biorthogonal

(biorthogonal (biorx_x), including fbi (bior4_4 (fbi))), where x indicates the

order of the wavelet. The higher the order, the smoother the wavelet. The


denoising and compression. The biorthogonal wavelets usually have the



use the wavelet design express vi to design the wavelet you want, bundle

the resulting analysis and synthesis filters, and then wire them to the filter

banks input.




banks for the wavelet you specify. If you specify a value for filter banks,


37

this vi ignores the settings in the wavelet input. You can use the wavelet

design express vi to design the analysis filters and the corresponding

synthesis filters.

o Analysis filters specifies the coefficient of the low pass analysis


� Low pass specifies the coefficient of the low pass analysis

filter, which this vi uses to compute the approximation

coefficients


analysis filter, which this vi uses to compute the detail

coefficients


filters and the high pass synthesis filters for the wavelet you

specify.

� Low pass specifies the coefficients of the low pass






• User defined thresholds specifies thresholds you define to rescale the

wavelet coefficients The length of user defined thresholds must

equal levels. The first element of user defined thresholds contains the

threshold for the detail coefficients at the largest level, and the last element

of user defined thresholds contains the threshold for the detail coefficients

at the first level. If you specify a value for user defined thresholds, this vi

keeps the approximation coefficients unchanged and ignores the settings

in thresholding rule, rescaling method, andoption for approx coefficient.


38

• Denoised signal returns the signal after denoising.


out functionality.

With NI LabVIEW Biomedical Toolkit and other related toolkits, such as

the Advanced Signal Processing Toolkit (ASPT) and the Digital Filter Design Toolkit

(DFDT), you can conveniently build signal processing applications for both stages,

including baseline wandering removing, noise cancellation, QRS complexes detection,

fetal heart rate extraction and etc. This article discusses typical ECG signal processing

methods based on LabVIEW.

3.3.9. Performing Feature Extraction On ECG Signals:

For the purpose of diagnosis, you often need to extract various features from the

preprocessed ECG data, including QRS intervals, QRS amplitudes, PR intervals, QT

intervals, etc. These features provide information about the heart rate, the conduction

velocity, the condition of tissues within the heart as well as various abnormalities. It

supplies evidence for the diagnoses of cardiac diseases. For this reason, it has drawn

considerable attention in the ECG signal processing field. This section mainly discusses

how to perform ECG feature extraction.

LabVIEW Biomedical Toolkit provides an ECG Feature Extractor VI, and also an

ECG Feature Extractor application for users to extract ECG features conveniently (Add

link for “How to use ECG Feature Extractor” here). You can select whether to detect

QRS only or to detect all supported ECG features, including R position, R amplitude, iso

level, QRS onset, QRS offset, P onset, P offset, T onset and T offset.


39

Figure 3.5 ECG multiresolution analysis and implementation of QRS detection

The ECG Feature Extractor firstly detects all beats (R waves) in the signal, and

then extracts other features for every beat. Thus the accuracy of detecting R waves is very

important. For normal ECG signals, they can be easily detected, as shown in Figure 8.

While abnormal morphology makes the detection difficult for ECG from patients with

some specific heart diseases. Thus sometimes you need to perform some signal

enhancement (preprocessing) before feature extraction. The preprocessed ECG signal is

used to detect position of R waves. After that, all other features will be extracted using

original signal, because the signal enhancement may change these features.


40

Fig. 3.6 ECG signal

Signal enhancement usually contains two steps: filtering and rectification. R

waves of human ECG usually have a frequency between 10-35Hz. Thus R waves can be

more obvious and easily for detection after filtering using a bandpass filter. Rectification

sometimes can further enhance the R waves to make them easier to detect. Absolute and

square are two common used rectification methods. Figure 9 shows the processing result

of an ECG signal with some negative R waves and very large T waves. It can be seen

that, after enhancement, all beats can be easily detected. Biosignal Filtering VI is used to

filter the signal. In ECG Feature Extractor application, a preprocessing settings window is

provided for users to perform the signal enhancement interactively.


41

Fig. 3.7 Signal Processing


42

Figure 3.8 Original ECG, ECG after filtering and ECG after peak/valley detection

After extracting the features, you can perform heart rate variability (HRV)

analysis on the R-R interval signal to demonstrate the state of the heart and nerve system.

In HRV Analyzer of LabVIEW Biomedical Toolkit, you can directly synchronize the RR

intervals from ECG Feature Extractor.

3.3.10. UDP VI and Functions:

Palette Object Description

UDP Close Closes a UDP socket.

UDP Multicast

Open

Opens a UDP multicast socket on the port. You must manually select

the polymorphic instance you want to use.

UDP Open Opens a UDP socket on the port or service name.

UDP Read Reads a datagram from a UDP socket, returning the results in data out.

UDP Write Writes to a remote UDP socket.

Table 3.12 UDP Palettes


43

Closing UDP socket:

Fig. 3.14 UDP close

• Connection ID is a network connection ref num that uniquely identifies the

UDP socket you want to close.

• Error in describes error conditions that occur before this node runs. With

the following exception, this input provides standard error in functionality.

This node runs normally even if an error occurred before this node runs.

• Connection ID out has the same value as connection ID. Do not wire this

output to other UDP functions.


out functionality.

UDP open:

opens a UDP socket on the port or service name. Close the socket with the UDP

Close function. Use the UDP Multicast Open VI instead of this function to open

connections capable of reading, writing, or reading and writing data to or from UDP

Multicast sockets. Use the UDP Write function to query the NI Service Locator for the

port number registered with the service name.

Fig.3.15 UDP Open

• Net address specifies on which network address to listen. Specifying an

address is useful if you have more than one network card, such as two

ethernet cards, and want to listen only on the card with the specified


44

address. If you do not specify a network address, LabVIEW listens on all

network addresses. This function broadcasts only on the default network

address.

Use the string to ip function to obtain the ip network address of the current

computer.

• Port is the local port with which you want to create a UDP socket.

• Service name creates a known reference for the port number. If you

specify a service name, LabVIEW registers the service name and the port

number with the ni service locator.

• Timeout ms specifies the time, in milliseconds, that the function waits

before the function completes and returns an error. The default value is

35,000 ms or 35 seconds. A value of -1 indicates to wait indefinitely.




UDP socket.

• Port returns the port number the function used. If the input port is not zero,

the output port number equals the input port number. Wire 0 to the port

input to dynamically choose an available UDP port the operating system

determines is valid for use. As defined by the internet assigned numbers

authority (iana), valid port numbers are between the range of 49153

through 65535. Well known ports are between the range of 0 through 1033

and registered ports are between the range of 1034 through 49151. Not all

operating systems follow the iana standard; for example, windows returns

dynamic ports between the range of 1034 through 5000.


out functionality


45

UDP read:

Reads a datagram from a UDP socket, returning the results in data out.The

function returns data when it receives any bytes, and waits the full timeout ms only if it

receives no bytes.

Fig.3.16 UDP Read


UDP socket.

• Max size is the maximum number of bytes to read. The default is

548. (windows) if you wire a value other than 548 to this input, windows

might return an error because the function cannot read fewer bytes than are

in a packet.

• Timeout ms specifies the time, in milliseconds, that the function waits for

bytes. If no bytes have been received within the specified time, the function

completes and returns an error. The default is 35,000 ms. A value of –1

indicates to wait indefinitely.



• Connection ID out returns the same value as connection ID.

• Data out contains the data read from the UDP datagram.


out functionality.

• Port is the port of the UDP socket that sent the datagram.

• Address is the address of the computer where a datagram originates.


46

UDP Write:

Writes to a remote UDP socket.

Fig.3.17 UDP Write

• Port or service name can accept a numeric or string input. Port or service

name identifies what port to write to. If you specify a service name,

LabVIEW queries the ni service locator for the port number that the server

registered.

• Address is the address of the computer where you want to send a

datagram.

• Connection id is a network connection ref num that uniquely identifies the

UDP socket.

• Data in contains the data to write to another UDP socket. In an Ethernet

environment, restrict data to 8193 bytes. In a local talk environment,

restrict data to 1458 bytes to maintain gateway performance.

• Timeout ms specifies the time, in milliseconds, that the function waits

before the function completes and returns an error. The default value is

35,000 ms or 35 seconds. A value of -1 indicates to wait indefinitely.



• Connection id out returns the same value as connection id.


out functionality.


47

3.3.11. Flatten To String Function:

Converts the anything input to a flattened data string of binary values. You also

can use this function to convert the byte order, or ending format, of the data in the

flattened data string.

Fig.3.18 Flatten to string

• Anything can be any data type.

• Prepend array or string size? Indicates whether LabVIEW includes data

size information at the beginning of data string when anything is an array

or string. If prepend array or string size? Is FALSE, LabVIEW does not

include the size information. The default is TRUE. Prepend array or string

size? Only controls the top-level data size information. Arrays and strings

in hierarchical data types such as clusters always include size information.

• Byte order sets the ending format of the data in the resulting flattened

string. Byte order, or ending form, indicates whether integers are

represented in memory from most significant byte to least significant byte

or vice versa.

0 Big-ending, network order (default)—The most significant byte occupies the

lowest memory address.

1 Native, host order—Uses the byte-ordering format of the host computer.

3 Little-ending—The least significant byte occupies the lowest memory address.

Table 3.13 Byte order for Flatten to string



• Data string is the flattened data generated by the function. Data


48

string might contain header information before each non-scalar component

describing its size. Such a string can be stored in a file or sent over a

network. If you send the string over a network, the receiver must be able to

interpret it. Usually, LabVIEW stores data as noncontiguous, indirectly

referenced pieces. This function copies the data in LabVIEW form into a

contiguous buffer data string. Use the Unflatten From String function to

convert a data string back to any data type.

• Type string (7.x only) is visible only if the terminal is already wired from

a previous version of LabVIEW or if you right-click the function and

select Convert 7.x Data from the shortcut menu. Type string (7.x only) is

an encoded binary description of data string. Type string (7.x only) is not

the same as the type input in the Unflatten From String function. If type

string (7.x only) cannot represent the data type wired to anything, this

function returns an error.


out functionality.

Flatten To String Details

Use the Type Cast function for conversion of data types that does not result in

flattened data. If you need to flatten type descriptor data to a string that is readable by

LabVIEW 7.x or earlier, right-click this function and select Convert 7.x Data from the

shortcut menu. In Convert 7.x Data mode, this function shows the type string (7.x

only) output and displays the icon for this function with a red 7.x on it. LabVIEW 7.x and

earlier stores type descriptors in 16-bit flat representation. LabVIEW 8.0 and later stores

type descriptors in 33-bit flat representation. National Instruments recommends

reworking any application that uses the Convert 7.x Data mode as a long term solution.

If you need to flatten Boolean data to a string that is readable by LabVIEW 4.x or earlier,

right-click this function and select Convert 4.x Data from the shortcut menu.

The Convert 4.x Data shortcut menu item is visible only if you wire Boolean

data to anything. In Convert 4.x Data mode, this function writes the data in LabVIEW

4.x data storage layout and displays the icon for this function with a red 4.x on it.


49

LabVIEW 4.x and earlier stores Boolean data in two bytes unless the data is in an array,

in which case LabVIEW stores each Boolean element in a single bit. LabVIEW 5.0 and

later stores Boolean values in a single byte, regardless of whether it is in an array.

National Instruments recommends reworking any application that uses the Convert 4.x

Data mode as a long term solution.

If you use this function to flatten variant data, LabVIEW flattens the variant and

all its contents, including attributes. However, if you use the Variant To Flattened

String function, LabVIEW flattens only the variant and discards any attributes.

If you use this function to flatten data from a custom control or indicator that you saved

as a type definition, the function strips the type definition of its type definition wrapper.

If you do not want to strip this wrapper, right-click the function and select Expose

Typedefs from the shortcut menu. The Expose Typedefs shortcut menu item is visible

only if you wire data to type string (7.x only).

3.3.12. Unflatten From String Function:

Converts binary string to the type wired to type. Binary string should

contain flattened data of the type wired to type. You also can use this function to specify

the byte order, or ending format, of the data in the binary string.

Fig.3.19 Unflatten from string function

• Type is a normally constructed LabVIEW type. Type is not the type

string output in the Flatten To String function.

• Binary string is a flattened string that is typically generated by the Flatten

To String function. If data includes array or string size? Is TRUE, binary

string contains header information describing its size.

• Data includes array or string size? Indicates whether LabVIEW reads

data size information from the beginning of an incoming array or string.


50

If data includes array or string size? Is TRUE, LabVIEW reads the size

information from the beginning of binary string. If data includes array or

string size? Is FALSE, LabVIEW detects the size information from the

memory size of binary string. The default is TRUE. Data includes array or

string size. Only accesses the top-level data size information. Arrays and

strings in hierarchical data types such as clusters always include size

information.

• Byte order indicates the ending format of the data in the incoming

flattened string. Byte order, or ending form, indicates whether integers are

represented in memory from most-significant byte to least-significant byte

or vice versa.

0 Big-ending, network order (default)—The most-significant byte occupies the

lowest memory address.

1 Native, host order—Uses the byte-ordering format of the host computer.

3 Little-ending—The least-significant byte occupies the lowest memory address.

Table 3.14 Byte order for Unflatten to string



• Rest of the binary string contains any leftover bytes that this function did

not convert. This function does not convert all the bytes if the size of binary

string is not a multiple of the size of type.

• Value returns binary string as unflattened data of the same data type and

structure as type.


out functionality.


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Flowchart at client:

Fig.3.20 Flowchart for Client

Open UDP port

Read data from the

port.

Read number of bytes

received and add them to

the ones read in a previous

iteration

Concatenate data

received, and display

it in the string

indicator

Concatenate data

received, and display

it in the string

If the UDP Read.vi

times out, reset the

error cluster so an

error does not show

Check for errors

stop

Start


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CHAPTER 4

RESULTS

4.1 Results:

Fig. 3.9 Peak Plot of R-R interval and Heart Rate


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Fig. 3.10 Client side


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CHAPTER.5

CONCLUSION

5.1 Conclusion:

This project ‘ECG monitoring system’ deals with a transfer of ECG data from the

patients to the doctor present in his cabin. The idea of our project is firstly taking up an

ECG signal from the patient and reading the data in PC using an arduino (an open-source

electronics prototyping platform based on flexible, easy-to-use hardware and software)

board. Then the signal is applied to noise removal, base wander removal and mplification

circuits in labVIEW (a system design software) software. Now the ECG signal thus

obtained is sent from the patients recording system to doctor’s using an Ethernet cable or

LAN connection. This enables the monitoring of any number of patients by doctor at one

place.

5.2 Future Scope:

• Transmission of signal to a remote PC using a dial up modem

• Optical isolation of power supply and data communication interface

• Development of analog subsystem to replace the analog ECG machine

• Acquisition of signals other than ECG

• Using the same ECG aquistion we can find the QRS peaks and find heart rate an

detect some more cardiac diseases like sinus arythmia, myocardial infarctions etc


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REFERENCES:

• International Journal of Computing Science and Communication Technologies

• http://www.academia.edu

• International Journal of Computer and Electrical Engineering

• J.W. Zheng, Z.B. Zhang, T.H. Wu, Y. Zhang, A wearable mobihealth care system

supporting real-time diagnosis and alarm, Med. Bio. Eng. Comput. 45 (2007)

• Cosmanescu, B. Miller, T. Magno, A. Ahmed, I. Kremenic, Design and

implementation of a wireless (Bluetooth (R)) four channel bio-instrumentation

amplifier and digital data acquisition device with user-selectable gain, frequency,

and driven reference, in: Conf. Proc. IEEE Eng. Med. Biol. Soc., 2006

• Perkusich, G. S. Deep, M. L. B. Perkusich, and M. L. Varani, (1989) An expert

ECG acquisition and analysis system, IMTC- 89, 184-189.

• J. Olansen, E. Rosow, Virtual Bio-Instrumentation, Biomedical, Clinical and

Healthcare applications in LabVIEW, Publishing House Prentice Hall PTR, 2002

• M. Unser, A. Aldroubi and M. Eden, “ On the asymptotic convergence of B-

spline wavelets to Gabor functions”, IEEE Trans. Inform. Theory 38 (2), 864-872,

1992

APPENDIX:

Ecg en Labview

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