Survey on ECG Denoising and Disease Detection

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 04, 2016 | ISSN (online): 2321-0613

Survey on ECG Denoising and Disease Detection Sneha L.Patne1 Mrs.S.R.Khope2 1 Student 2Faculty 1,2 Department of Computer Engineering 1,2 G.H.Raisoni Institute of Engineering and Technology, Wagholi-Pune, India Abstract— This paper presents, an effective software tool for cardiac disease detection based on Matlab GUI.ECG signals are non-stationary signals and get affected by noise, so the noise is removed from the ECG signal which is called Denoising. For denoising of ECG signal discrete wavelet transform is applied. This denoised ECG signal is used to extract the features such as QRS complex, R peak and R-R interval. To extract the QRS complex from the ECG signal Pan Tompkins algorithm is used. These features are used to diagnose the cardiac arrhythmias such as bradycardia and tachycardia. Using Matlab GUI the results are displayed. An automatic generated Email which contains the screen capture of GUI panel is sent to the doctor who may be at remote location.

Key words: ECG, Denoising, Discrete Wavelet Transform, Pan Tompkins Algorithm I. INTRODUCTION The Electrocardiogram represents electrical activity of heart.

The Electrocardiogram is generated by measuring the electrical potential between different points. The potential difference is measured by placing electrodes on the surface of body. The frequency range of ECG is between 0. 1-150 HZ. The typical ECG signal contains 5 waves namely P wave, Q wave, R wave, S wave and T wave as shown in figure 1.By using these feature we can detect whether the patient is having any heart disease or not and also the type of disorder can be diagnosed. The ECG signal is non stationary in nature and easily gets affected by noise. There are different sources of noise by means of which ECG signal can get interfered such as noise generated from external sources to the patient and noise generated from the patient.

Noise generated from external sources includes motion artifacts, power line disturbances, Baseline Wander (noise due to improper placement of electrodes).Noise generated from patient is muscle artifacts. The noisy ECG signal may lead to wrong interpretation and incorrect diagnosis.

Fig. 1: Typical ECG signal

Due such wrong interpretation false alarm may get generated. By analyzing ECG signal properly the heart disease can be diagnosed. But the visual analysis of ECG is very time consuming and complex task to do. As the symptoms of irregular activity of heart due to arrhythmia does not occurs whole day, they occurs at random hours in a day. This is very time consuming job for doctors which encourages the automatic diagnostic tool concept. Now a days doctors are preferring such tools to diagnose the diseases which reduces both complexity and time in diagnosis process.

II. LITERATURE SURVEY Kaiyu Zhang, Lixin Song and Di Lu [1] presented the “Design of ECG Monitoring System based on GPRS “Which describes the work in remote ECG monitoring system and wireless data transmission.

This system consists of MSP430F2148 processor as a controller. For wireless communication SIM 300 GPRS module is used. The system is divided into three basic modules: ECG acquisition module, wireless transmission module, doctor workstation module. The ECG acquisition module includes input protection and preamplifier, low pass filters, post amplifier and so on. The input protection and preamplifier circuit is used to remove noise from ECG signal as it weak and vulnerable to noise.

The instrumentation amplifier with high common mode rejection ratio is required so AD620AN used.SIM 300 GPRS module is used for wireless communication which is controlled by MSP4230F2418 through AT commands.At doctor workstation VB6.0 and SQL 2000 development tool with Winsock programming technique is used to plot the ECG. But the use of GPRS increases the hardware required and also increases the complexity.

Naazneen M. G., Sumaya Fathima, Syeda Husna Mohammadi, Sarah Iram L. Indikar, Abdul Saleem, Mohamed Jebran[12] Presented the “Design and Implementation of ECG Monitoring and Heart Rate Measurement System” which describes 3-lead Electrocardiogram monitoring system along with heart rate measurement.

The acquisition of physical ECG signal is done by placing Ag/Cl electrode to the arms and right leg of patient.

Instrumentation amplifier which has gain of 1000 is used to remove noise from ECG signal and low pass filters with cut off frequency 150 HZ. For calculation of heart rate microcontroller AT89S52 is used and the results i.e. the heart rate count is displayed on LCD.In this paper the advantage is low cost amplifier and filters are used but the data transmission is not done.

Nisha singh [13] presented “Microcontroller Based Wireless Transmission on Biomedical Signal and Simulation in MATLAB” using Bluetooth technology.

In this system Wireless transmission of biomedical signals like ECG, heart rate, blood pressure are

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Survey on ECG Denoising and Disease Detection (IJSRD/Vol. 4/Issue 04/2016/345)

measured and recorded at a central monitoring system. This data is processed at the central monitoring computer. It is very crucial to send the patient’s data from his personal bed side monitoring system to the central monitoring system in the hospital. In this system for the data transmission Bluetooth technology is used. Using Bluetooth the data transmission is done wire lessly. This will help to monitor the number of patients in the central place of hospital. For acquisition of ECG signal ECG amplifier is constructed with gain of 1000 and CMRR 80 DB along with frequency bandwidth 0.05-100HZ.Using MATLAB the features of ECG signals are extracted. Peaks are detected by applying FFT to the signal and the R-R interval is calculated. Use of Bluetooth technology has the limitation of range of Bluetooth which is very small.

P. Keerthi Priya [6] Presented the “MATLAB Based GUI for Arrhythmia Detection Using Wavelet Transform” which describes a software tool for analysis of ECG signal and diagnose the disease using MATLAB. This system mainly involves 3 steps: preprocessing of ECG signal, feature extraction, arrhythmia classification. In pre-processing, noise from ECG signal removed also normalization is done and DC offset is removed. After removing noise from ECG signal, feature extraction is carried out. For extracting features discrete wavelet transform is applied to the signal. Finally after extracting the features i.e. PR,RR intervals and QRS complex are calculated. In the classification step different decision rules are set depending upon the intervals. This software tool is very effective and flexible but data transmission is not done here.

Sherin Sebastian, Neethu Rachel Jacob, Yedu Manmadhan, Anand V. R. ,M. J. Jayashree [11] presented “Remote Patient Monitoring System” in this system an image based continuous data streaming of ECG signal using digital camera for image capturing is done using MATLAB. This image is send to the doctor via internet. In case of any anomaly a notification is sent to the doctor’s phone.

Fig. 2: System Block Diagram In this system, the ECG signal taken from the database of MIT-BIH which includes ECG signal with normal cardiac activity, tachycardia and bradycardia symptoms. The acquired signal is affected by noise so at the first stage noise is removed from the signal. This is called as Denoising of ECG signal. For denosing of ECG signal discrete wavelet transform is applied. Now the noise free ECG signal is applied to Pan Tompkins algorithm which leads to feature extraction. Here QRS complex is detected.

By detecting the R peaks the R-R interval is calculated.

Some decision rules are set for classification of different types of arrhythmias. By applying the decision rules the software tool will diagnose the cardiac arrhythmia disorder.

MATLAB GUI will help to display the waveforms and diagnosis report. The screen capture of the MATLAB GUI will be sent to the doctor at remote location.

V. CONCLUSION This project gives an efficient and flexible software tool to analyze the ECG signal and diagnose the type of arrhythmia.

It is low cost and less complex software tool having user friendly approach. The diagnosis report is sent to doctor through an automatic generated E-mail which is the fastest and easiest media of communication. Sending an E-mail is the secure and confidential way.

III. NEED

ACKNOWLEDGEMENT

A. Diagnosis Purpose The behavior of ECG signal is non stationary. The system of cardiac arrhythmia does not show up whole day. It is very time consuming job for doctors to examine the heart activity of patient a day.So an automatic diagnostic tool should be there to detect such activity of heart and classify the arrhythmia.

Author is extremely thankful to research Guide” Prof.

Mrs.S.R.Khope”, G.H.Raisoni Institute of Engineering and Technology, Pune for consistent guidance ,inspiration and her valuable support.

I am also grateful to college Principal “Prof.Dr.R.D kharadkar” and HOD of electronics telecommunication department “Prof.N.B.Hulle”. Also thankful to P.G.CoOrdinator Prof. Mrs.M.R.Bachute for their time to time support and guidance.

B. Transmission Purpose Now a days doctors are preferring automatic diagnostic tools with wireless data transmission. This helps to reduce the frequent visiting of doctor and patient.

IV. PROPOSED WORK The basic system block diagram of this system as shown below:

REFERENCES [1] Wu Lihua, Li, Zhao Shuetal. Design and Development of Remote ECG Monitoring System based on Sim300.

Journal Of Harbin University Of Science And Technology, 2010, 15(1):112-115 [2] Kaiyu Zhang. The Research on Remote ECG Monitoring System Based on GPRS. Harbin University of Science and Technology,2008.

[3] Zhen Huang. Research on Technology for Telemetry ECG Detection.Harbin Institute of Technology,2006

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[4] SAHAMBI JS. Using wavelet transforms for ECG characterization. IEEE BME, 2001, (5) 77-83 [5] P. J. Tompkins, “A real-time QRS detection algorithm”, IEEE Trans Biomed Eng,vol.31 (3) pp .230-236, 1985.

[6] P.Keerti Priya,”Matlab based GUI for arrhythmia detection using wavelet transform,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 4, Issue 2, February 2015.

[7] Jaylaxmi C Mannurmath,”MATLAB Based ECG Signal Classification,” International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014.

[8] M. K. Islam, A. N. M. M. Haque, G. Tangim, T. Ahammad, and M. R. H. Khondokar,” Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools,” International Journal of Computer and Electrical Engineering, Vol. 4, No. 3, June 2012 [9] Shital L. Pingale, Nivedita Daimiwal,”Detection of Various Diseases Using ECG Signal in MALAB,” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-3, Issue-1, March 2014.

[10] Zarina Md Amin, Suryani Ilias, Zunuwanas Mohamad,” Electrocardiogram (ECG) Monitoring System using Bluetooth technology,” International Journal of Information and Electronics Engineering, Vol. 5, No. 4, July 2015 [11] Sherin Sebastian, Neethu Rachel Jacob, Yedu Manmadhan, Anand V. R., M.J.Jayashree” International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.5, September 2012.

[12] Naazneen M. G., Sumaya Fathima, Syeda Husna Mohammadi, Sarah Iram L. Indikar, Abdul Saleem, Mohamed Jebran ”Design and Implementation of ECG Monitoring and Heart Rate Measurement System”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013.

[13] Nisha Singh” Microcontroller Based Wireless Transmission on Biomedical Signal and Simulation in MATLAB”, IOSR Journal of Engineering e-ISSN: 2250-3021, p-ISSN: 2278-8719 Vol. 2, Issue 12 (Dec. 2012), ||V4|| PP 08.

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