CONVERSION OF ECG GRAPH INTO DIGITAL FORMAT AND DETECTING THE DISEASE. G. Angelo Virgin 1, Dr.M.Sangeetha 2

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1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu CONVERSION OF ECG GRAPH INTO DIGITAL FORMAT AND DETECTING THE DISEASE G. Angelo Virgin 1, Dr.M.Sangeetha 2 1 Assistant Professor, ECE, Bharath University, Chennai, TN, India 2 Professor, Department Of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai-73, India 1 g.angelovirgin@gmail.com, 1 sangeetha.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 465

2 International Journal of Pure and Applied Mathematics detection of J point on the ECG, which is the inflection 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 1). 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 -1 - for Normal, -2 for Left bundle branch block beat and 1 for Right bundle beat and 2 for premature ventricular contraction [16-22]. [16 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 ia detection in the MIT-BIH MIT database can be used. The performance of ischemic beats episodes performance are often used [11 [11-15]. 3. Proposed Work 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 are ECG printout is relieve this problem. The example of paper based ECG figure.1 Figure 3. Generic ECG digitization process Scanning ECG paper recordings are scanned. The scanning 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 n the data. In order to retrieved the information from the ECG image[23-30]. 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 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 signal are target of the processs 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. 466

3 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 467

4 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; 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 periodically 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 5. ECG signal Data base 4. Disease Detection Using ECG Data 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 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 overview of the algorithm as follows. Amplitude-: Figure 6. ECG Signal 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 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-wave interference. Implementation of SVM for QRS detection in ECG signal is done by LIBSVM software.libsvm is an integrated software package for support vector 468

5 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. Figure 10. SVM Classifier 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 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. References [1] Vijayaragavan S.P., Karthik B., Kiran T.V.U., Sundar Raj M., Robotic surveillance for patient care in 469

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