CONVERSION OF ECG GRAPH INTO DIGITAL FORMAT AND DETECTING THE DISEASE

G. Angelo Virgin1, Dr.M.Sangeetha2

1Assistant Professor, ECE, Bharath University, Chennai, TN, India

2Professor, Department Of Electronics and Communication Engineering,

Bharath University, Selaiyur, Chennai-73, India 1 g.angelovirgin@gmail.com,

1sangeetha.ece@bharathuniv.ac.in

Abstract:Electrocardiogram (ECG) is the most

important and widely used method to study the heart

related diseases. The detailed study of ECG graph by

the medical practitioner helps him to understand and

identify the condition of the heart. Based on the

information retrieved from the ECG graph the patient

can be given proper treatment. The person having a

medical history of heart ailments will have to

maintain a record of all the ECG papers for timely

analysis and diagnosis of the diseases. This process

requires large storage space and extensive manual

effort. The conventional technique of visual analysis

to inspect the ECG signals by doctors or physicians

are not effective and time consuming. Therefore, an

automatic system which involves digital signal

integration and analysis is required. We presents a

digital signal processing tool developed using

MATLAB, which provides a very low-cost and

effective strategy for Analog to Digital conversion.

This digitization process is generated using gradient

based feature extraction algorithm without using a

dedicated hardware. In addition, this tool can be used

to potentially integrate digitized ECG information

with digital ECG analysis programs and with the

patient's disease analysis.

Keywords: ECG, Compression, Segmentation, image

retrieval, Digitization, Laplacian filtering.

1. Introduction

Digital equipment are flexibility of working their

output. Electro cardiogram signals recorded to digital

format. Therefore digital signals are high signal

processing retrieval for the information. The

advantages of ECG signal are digital technology for

turns to first- choice. Therefore digital technologies

are high cost for modern digital equipment. The

serious barrier are crossed by working analogical

device limitations. The analog devices are used to

convert the analog values for suitable interface to

digital microcomputer. The digitizing analogical data

are many areas, medicine history of patients would be

kept and case studies are correlated. The storage ECG

signals are inefficient data are processing for

unfeasible. The designing acquisition values are

interface to microcomputers and instead of purchasing

sophisticated computerized equipment. The trivial task

are assemble to design for ECG signals. The MATLAB

software tool is used analog version of signals and

spectra digitalized ECG scanning data file are efficiently

processed and stored. Therefore alternative approach for

A/D conversion without requiring specific hardware.

2. ECG Pattern Recognition

ECG processing proposed to pattern recognition,

parameter extraction, spectro- temporal techniques for

assessment for the heart’s status [7], de noising, baseline

correction and arrhythmia detection. Three basic waves

are P, QRS and T (Fig 1). These waves are far field

induced by specific electrical phenomena cardiac

surface, namely atrial depolarization (P), ventricular

depolarization (QRS) and ventricular depolarization (T).

Numerous techniques are developed to recognize and

analyse waves from digital filtering techniques are neural

network (NN) and spectro-temporal techniques. A great

deal of research devoted to digital processing of ECG

waves, are QRS, P and T wave detection (Fig 1). The P

wave detection is the problem (especially in the presence

of noise in the case of ventricular tachycardias).

QRS and T wave detection have been developed.

In this cases clinically acceptable results arrhythmia

analysis are received a great deal of attention, directed to

QRS and PVC classification and supra ventricular

arrhythmias. In shifted from arrhythmia to ischemia

detection and monitoring.

It causes depolarization for resting membrane

potential of ischemic region with respect to resting

membranee potential of normal region. The potential

difference between the flows of injury current is

manifested in the ECG by an elevated or depressed ST

segment (Fig 1) [10-14]. ST segment detection and

characterization of major problem of ECG processing

inhibiting factors are slow baseline drift, noise, sloped

ST, patient dependent abnormal ST depression levels,

and varying ST-T patterns in the ECG patient. A number

of methods proposed in the literature for ST detection

based on digital filtering, analysis of first derivative

signal, and syntactic methods. To measure specific

parameters in ways critically dependent upon the correct

International Journal of Pure and Applied MathematicsVolume 116 No. 15 2017, 465-471ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

465

detection of J point on the ECG, which is the

inflection point following the S wave. Where ST

segment is sloped, or the signal is noisy, it is

impossible to identify this point with reliability (Fig

1).

Figure 1. Normal and abnormal ECG Waveform.

The constituent ECG waves and J point. In the

ischemic ECG, we observe the ST elevation, and

observe the second beat that the J point is not easily

discernible. The biomedical signal processing are

based on standard in the normal case, annotated

databases and forming a common reference. The

database are ischemia detection in the MIT

database can be used. The performance of ischemic

beats episodes performance are often used [11

3. Proposed Work

The electrocardiogram is a signal functional

investigation. The electric signals are generated

human heart and create cardiac cycle generate blood

circulation. The basic components are P wave, QRS

complex and T wave as show in (fig 2) P wave is

generated atrium depolarization. The QRS complex is

generated for ventricle depolarization and T wave

generated from cardiovascular disease is one of the

leading causes of death. The lacking of physician

expert for analysis on ECG signal is serious problem

especially on rural area. Therefore ECG classification

are ECG printout is relieve this problem. The example

of paper based ECG figure.1

detection of J point on the ECG, which is the

ection point following the S wave. Where ST

segment is sloped, or the signal is noisy, it is

impossible to identify this point with reliability (Fig

Normal and abnormal ECG Waveform.

The constituent ECG waves and J point. In the

ECG, we observe the ST elevation, and

observe the second beat that the J point is not easily

discernible. The biomedical signal processing are

based on standard in the normal case, annotated

databases and forming a common reference. The

ia detection in the MIT-BIH

database can be used. The performance of ischemic

beats episodes performance are often used [11-15].

The electrocardiogram is a signal functional

investigation. The electric signals are generated from

human heart and create cardiac cycle generate blood

circulation. The basic components are P wave, QRS

complex and T wave as show in (fig 2) P wave is

generated atrium depolarization. The QRS complex is

generated for ventricle depolarization and T wave is

generated from cardiovascular disease is one of the

leading causes of death. The lacking of physician

expert for analysis on ECG signal is serious problem

especially on rural area. Therefore ECG classification

The example

Figure 2. Example image of paper based ECG printout.

In this paper proposed filters used to reduce

influence of noise, such as muscle noise, power line and

baseline wonder. New database are composed with each

type of beat annotations such as -

Left bundle branch block beat and 1 for Right bundle

beat and 2 for premature ventricular contraction [16

Figure 3. Generic ECG digitization process

Scanning

ECG paper recordings are scanned. The sca

resolution are 600/400/200 dots per inch. Preferred

algorithm is compression for the JPEG image. Figure1

represents scanned ECG paper recording.

Crop region of interest

ECG graph is selected the particular region of

interest to obtain the data. In order to retrieved the

information from the ECG image[23

Image Binarization

It is a process and translate for colour image into

a binary image. It is widespread process for image

processing and especially images are contain neither

artistic nor iconographic. In binarization operation

reduces to number of colours to a binary level are

apparent gains of storage and besides simplifying the

image analysis as compared to the true colour image

processing. The axis are composing the image and

applied binarization process by Otsu’s algorithm, since it

has been shown to provide satisfactory results in many

applications. Binarization is an important step for

moving from a biomedical map image towards a digital

signal are target of the process obtain a sequence of

values that corresponded amplitude of a uniform time

series.

Gradient based Feature Extraction

The gradient is a vector which has both

magnitude and directions.

Example image of paper based ECG printout.

In this paper proposed filters used to reduce

influence of noise, such as muscle noise, power line and

baseline wonder. New database are composed with each

-1 for Normal, -2 for

Left bundle branch block beat and 1 for Right bundle

beat and 2 for premature ventricular contraction [16-22].

Generic ECG digitization process

ECG paper recordings are scanned. The scanning

resolution are 600/400/200 dots per inch. Preferred

algorithm is compression for the JPEG image. Figure1

represents scanned ECG paper recording.

ECG graph is selected the particular region of

n the data. In order to retrieved the

information from the ECG image[23-30].

It is a process and translate for colour image into

a binary image. It is widespread process for image

processing and especially images are contain neither

tistic nor iconographic. In binarization operation

reduces to number of colours to a binary level are

apparent gains of storage and besides simplifying the

image analysis as compared to the true colour image

processing. The axis are composing the image and then

applied binarization process by Otsu’s algorithm, since it

has been shown to provide satisfactory results in many

applications. Binarization is an important step for

moving from a biomedical map image towards a digital

s obtain a sequence of

values that corresponded amplitude of a uniform time

Gradient based Feature Extraction

The gradient is a vector which has both

International Journal of Pure and Applied Mathematics Special Issue

466

De-Skewing

It is a common phenomenon can be appear

scanned image. Skewing image to an angle and

results are rotated image. Hough Transform used to

de-skew image. Skew angle is calculated using

background grid lines of scanned ECG image.

Image Enhancement

In this step enhances to ECG image by

making the signal lines. The filtering is applied to

making background noise lighter than the ECG signal.

Threshold values are chosen by comparing between

noise pixels representing actual ECG signal. ECG

signal pixel values are closer threshold and pixels are

made by subtracting fixed value. The noise pixels are

close to the threshold, will be made lighter by adding

a fix value. Therefore image will contain distinct

ECG signal in the image.

Colour Based Segmentation

To remove background grid of an ECG

lighter shade of colour actual signal waveform are

processed column by column. The darkest pixels are

extracted each column and row are replaced black

pixel and rest of the pixels as pure white. However

lead to extraction undesired printed character as well.

Fig 4 shows output of colour based segmentation; it

has some printed characters also.

Region Based Segmentation

Removes the isolated pixels that do not

represent the signal. The following steps are

Step 1: Remove the frame of image is border of the

input image.

Step 2: Scan the input ECG image is column by

column.

Step 3: Repeat each column:

a. Extract each black pixels

b. In case of isolated pixels delete them.

c. If more than one black pixel:

i. Algorithm checks previous and next columns. They

contain one pixel, then the current column select pixel

that likely goes flow of signal pattern.

ii. The next column contains the same problem of

having black pixel for the current column select the

middle pixel that lies exactly in the middle of the

black pixel pattern.

Step 4: Save index of the picked pixel. The process of

choosing middle value in step 2 is performed by

following algorithm:

Step 4.1: The centres of different regions

Step 4.2: separate regions and compute centroid of each

region.

Figure 4. ECG Steps

1a: The threshold value region are separation. If

current_point>previous_sample + threshold_value add

centre.

Step 5: Pick centre point value of closest to previous

column pixel index. In this centres calculated in step 2

are used. To find the centre that has minimum difference

between previous column pixel and that next centre.

Signal Representation

The ECG signals are corresponding column with

pixel. Finally the signals are saved as a txt file.

Median Filtration

Actual ECG signal recorded an ECG machine

can be degraded by presence of noise. Various sources of

noise are Power Line Interface (50Hz or 60Hz noise

from power lines) Baseline wander.

Muscle noise

Other interference noise signal are misdiagnosis

desirable to remove noise and the actual ECG signal. A

noisy ECG signals are taken the performance of various

filters including median filter. FIR filters are compared

to found that median filter has best result both by visual

inspection and quantitative measurement.

Axis Identification

It is different types of data plotting are analysed

for the sake of generality of the methodology proposed.

This paper used to register the data contain grid and box.

One horizontal line and one vertical line but each case

specific analysis needs to perform adequately interpret

the signal obtained, compensating offsets, etc.

Pixel- to- Vector Conversion

A better data retrieving from image vectors and

twice the number of columns pixels are analysed, some

peaks are found successive vertical black lines are low

International Journal of Pure and Applied Mathematics Special Issue

467

resolution images. Each component vector is a

complex number. There are two components are same

column and identical real part and the column index

of pixel matrix. The two consecutive imaginary parts

are quantify the upper and lower limits of vertical

black line. For instance of 2D-vector V=[…; 11+25i;

11+26i; has coordinates meaning that vertical black

line. Fig 4 deals with ECG chart with no axis. A

stretch vector are used to plotting Figure is V =

[10+26i; 10+26i;

Figure 5. ECG signal Data base

4. Disease Detection Using ECG

Pan Tompkins Algorithm

The Pan Tompkins influence to QRS detection as

compared to others. A literature survey signifies the

important algorithm in detecting QRS peak [4]. The

accuracy of Electrocardiogram waveform extraction

plays vital role in better diagnosis of heart related

illnesses. Normal ECG consists of P wave, QRS

complex and T wave. The ECG waves are reflect to

heart’s activity are P wave muscle contraction of atria

and indicates atrial enlargement. Q wave negative

value are supposed to be 45% less than R wave value

[4].

Figure 6. ECG Signal

Amplitude-:

P wave: 0.45mV

R wave: 1.6 mV

Q wave: 45 percent of R wave

T wave: 0.1 to 0.5 mV Duration -:

P-R Interval: 0.14 to 0.40 sec

Q-T Interval:0.45 to 0.44 sec

S-T Interval:0.05 to0.15 sec

P wave Interval:0.11 sec

QRS Interval: 0.09 sec

resolution images. Each component vector is a

complex number. There are two components are same

l part and the column index

of pixel matrix. The two consecutive imaginary parts

are quantify the upper and lower limits of vertical

black line. For instance of 2D-vector V=[…; 11+25i;

11+26i; has coordinates meaning that vertical black

ith ECG chart with no axis. A

stretch vector are used to plotting Figure is V =

ECG signal Data base

Disease Detection Using ECG Data

The Pan Tompkins influence to QRS detection as

literature survey signifies the

important algorithm in detecting QRS peak [4]. The

accuracy of Electrocardiogram waveform extraction

plays vital role in better diagnosis of heart related

illnesses. Normal ECG consists of P wave, QRS

ECG waves are reflect to

heart’s activity are P wave muscle contraction of atria

and indicates atrial enlargement. Q wave negative

value are supposed to be 45% less than R wave value

:

Pan and Tompkins algorithm identifies the QRS

complex based digital analysis slope, amplitude, and

width of ECG data. This algorithm implements to digital

band pass filter. It can reduce false detection and caused

by various types of interference in the ECG signal.

In this algorithm adjusts parameters

adapt the changes in QRS morphology and heart rate.

The following processing steps are given by

• Band-pass filtering.

• Differentiation.

• Squaring.

• Moving window integration.

• Thresholds adjustment.

Figure 7. QRS complex detection.

P wave is rounded peak and occurred QRS

complex. Therefore P wave found based on the location

of QRS complex. The QRS detection algorithms

included all biomedical signal processing textbooks are

introduced Pan and Tompkins. An

algorithm as follows.

Figure 8. Pan-Tompkins beat detection algorithm

Fig 8 shows graphical representation of the basic steps of

algorithm. The ECG signal passes through filtering,

derivation, squaring, and integration phases before

thresholds set and QRS complexes are detected. In ECG

algorithm passes through a low pass and high pass filter

in order to reduce influence of the muscle noise, power

line interference, baseline wander and the T

interference.

Implementation of SVM for QRS

ECG signal is done by LIBSVM software .LIBSVM is

an integrated software package for support vector

Pan and Tompkins algorithm identifies the QRS

mplex based digital analysis slope, amplitude, and

width of ECG data. This algorithm implements to digital

band pass filter. It can reduce false detection and caused

by various types of interference in the ECG signal.

In this algorithm adjusts parameters periodically

adapt the changes in QRS morphology and heart rate.

The following processing steps are given by

QRS complex detection.

P wave is rounded peak and occurred QRS

complex. Therefore P wave found based on the location

of QRS complex. The QRS detection algorithms

included all biomedical signal processing textbooks are

introduced Pan and Tompkins. An overview of the

Tompkins beat detection algorithm

Fig 8 shows graphical representation of the basic steps of

algorithm. The ECG signal passes through filtering,

derivation, squaring, and integration phases before

holds set and QRS complexes are detected. In ECG

algorithm passes through a low pass and high pass filter

in order to reduce influence of the muscle noise, power

line interference, baseline wander and the T-wave

Implementation of SVM for QRS detection in

ECG signal is done by LIBSVM software .LIBSVM is

an integrated software package for support vector

International Journal of Pure and Applied Mathematics Special Issue

468

classification and distribution estimation. A modified

sequential minimal optimization algorithm to perform

training of SVMs. SMO algorithm breaks into the

large quadratic programming (QP) problem in to a

series of smallest possible QP problems. The small

QP problems are solved analytically, which avoids

using a time-consuming numerical QP optimization

problem as an inner loop [8].

Performance: We have used three parameters for

evaluating performance of our algorithm. The

parameters are accuracy, sensitivity, positive

predictive. The parameters are defined using

measures True Positive (TP), True Negative (TN),

False Positive (FP), and False Negative (FN)

True Positive: arrhythmia detection coincides with

decision of physician.

True Negative: both classifier and physician

suggested absence of arrhythmia

False Positive: system labels a healthy case as an

arrhythmia one

False Negative: system labels an arrhythmia as

healthy

Accuracy: Accuracy is the number of correctly

classified cases, and is given by

Accuracy= (TP+TN) / N----------------------- (1)

Total number of cases are N

Sensitivity: Sensitivity refers to the rate of correctly

classified positive. Sensitivity may be referred True

Positive Rate. Sensitivity should be high for a

classifier

Sensitivity = TP / (TP+FN) -------------------- (2)

Positive predictive: Positive predictive is probability

that disease is present when test is positive, which is

by how much amount disease is correctly predicted.

Positive predictive = TP/ (TP+FP) ---- (3)

5. Results

Figure 9.QRS Complex Detection

Figure 10. SVM Classifier

Figure 11.Type of Disease

6. Conclusion

The proposed method extraction and digitization of ECG

signal from various sources. They are thermal ECG

printouts, scanned ECG and captured ECG images are

from devices is proposed. A reasonably accurate

waveform are free from printed character and as tested

through heart rate, QRS width and stability calculations.

We used in this paper MATLAB software for the Pan

and Tompkins QRS detection algorithm for detection of

diseases and related to heart. Notwithstanding fairly

amount of scientific results and proposed work describes

the foundation of efficient tool to generate digital data

signals legated paper charts. In this paper proposed to

low-cost software tool that can be particularly helpful to

scientists and engineers. It can save data as MATLAB

file or as ASCII files and edit or complement the data.

Further work progress are enhance to overall

efficiency of the method and developing a fuzzy based

expert system that assist a doctor in diagnosis and

generating automatic diagnosis reports as well.

Acknowledgement

I would like to express my special thanks of gratitude to

my Supervisor as well as our principal who gave me the

golden opportunity to do this wonderful project on the

topic, which also helped me in doing a lot of Research

and I came to know about so many new things I am

really thankful to them.

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