ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 10, April 2013

Transcription

1 ECG Processing &Arrhythmia Detection: An Attempt M.R. Mhetre 1, Advait Vaishampayan 2, Madhav Raskar 3 Instrumentation Engineering Department 1, 2, 3, Vishwakarma Institute of Technology, Pune, India Abstract The aim of this article is to present an effort to detect and diagnose the heart arrhythmia present in the patient by recording ECG(Electrocardiograph). After collecting information from different hospitals it was found that there is a need of an expert system, which can help the subordinate medical staff to detect arrhythmias. So for this purpose software is developed in MATLAB, which can detect some abnormalities in the patient s heart. For testing MIT arrhythmia database has been used. For calculating different ECG parameters, Pan Tompkins algorithm has been modified and used. This software can be immensely helpful to the medical fraternity. An attempt is tried to provide a treatment plan for the more risky and frequently occurring arrhythmias. to MATLAB s many features like easy GUI building and various toolboxes. PATIENT Index Terms ECG, Arrhythmia, Pan Tompkins algorithm, treatment plan, MATLAB GUI. I. INTRODUCTION The heart is endowed with a special system (a) for generating rhythmical impulses to cause rhythmical contraction of the heart muscle, and (b) for conducting these impulses rapidly through the heart [2]. Unfortunately, though, this rhythmical system of the heart is very susceptible to damage by heart disease. The activity of the heart s electrical system can by observed by means of electrocardiography. Analyzing the ECG records is a difficult and tedious, even for experienced physicians. The waves that are recorded by an ECG machine vary greatly; from patient to patient, for different leads, and they may vary even for the same patient within short time interval. Additionally, the ECG records may be corrupted by many kinds of noise produced by electrical devices used for recording. However, an ECG is a valuable source of information regarding activity of patient s heart. The rhythm disturbances, arrhythmias, are of special interests at the ICU for cardiac patients because some of them require immediate medical care and prompt detection can avoid dangerous circumstances. Therefore, monitoring the ECG recordings, with fast and reliable arrhythmia detection, and treatment plans are of great importance in the ICU. An automated computer system that performs this task will be a big help to the ICU staff. The system used in this paper is described by fig1. The ECG is acquired with electrodes placed on patients and the output of the data acquisition block is fed into the data treatment block. Here the ECG is de-noised and the features like the various peak positions and the amplitudes and intervals are extracted. The code is written in MATLAB due DATA ACQUISITION DATA TREATMENT & FEATURE EXTRACTION DETECTION AND DIAGNOSIS Fig.1 Proposed System Blocks The system is divided into three modules as shown in fig 2. Very first there is the input block from where we get the ECG data. It can sit on a database as well as go online by accepting real time data. The Diagnosis module consists of the expert system and is based on fuzzy logic. A treatment plan is provided that is flashed for some of the more important arrhythmias. The most important module in this is the ANALYSIS module. The analysis module mainly processes the signal. The signal is filtered and the noise is removed in this module. Feature extraction and shape classification is carried out in this module. 272

2 Start ECG signal Pan-Tompkins algorithm R-peak Fig 2: system description Beat Location QRS shape classification II. ECG SIGNAL PROCESSING For monitoring applications such as for intensive care patients, the bandwidth of ECG is restricted to Hz. In these environments, rhythm disturbances (i.e., arrhythmias) are principally of interest rather than subtle morphological changes in the waveforms. First, a digital band pass filter (combination of low pass and high pass filter) is applied to filter out the noise components. Second, the signal is analyzed using Pan-Tompkins algorithm for detection of QRS complex. Next, using the beat location, the features of the QRS complex are extracted. The features are beat duration, average RR interval, variance, time since last QRS elapsed, number of ventricular complexes Finally, using these features, the beat is classified as either normal or ventricular. As shown in fig 3 Pan-Tompkins Algorithm Pan-Tompkins algorithm [1] proposes a real-time QRS detection based on analysis of slope, amplitude, and width of QRS complexes. It includes a series of filters and methods that perform low pass, high pass, derivative, squaring, integration, and adaptive threshold and search procedures. Finally, by analyzing the original signal the position of the QRS complex is detected. The QRS detection algorithm consists of the three following processing steps: 1) Linear Digital Filtering 2) Non Linear Transformation 3) Decision Rule Algorithms QRS shape Fig 3: Algorithm for ECG processing The linear digital filtering consists of a band pass filter, derivative filter and a moving average integrator. The nonlinear transformation process is that of signal amplitude squaring. Adaptive thresholding and T wave discrimination provide the decision algorithms. Though most of the QRS detection algorithms depend on slope of R wave, this is not sufficient. The following parameters are calculated in addition to slope R wave for proper QRS detection: Amplitude, width and QRS energy. This algorithm is a single channel algorithm. The algorithm is divided into three processes: 1) Learning Phase 1: To initialize detection thresholds based upon signal and noise peaks detected during learning process. 2) Learning Phase 2: It requires two heartbeats to initialize RR interval average and RR interval limits. 3) Detection Phase: Recognition Process The basic algorithm is Pan Tompkins algorithm but some modifications are done (fig 4) so that parameters other than QRS complex can be obtained. It has following steps, - Detection of R wave - Deletion of R wave -Calculation of other waves and time intervals. Various steps in the software are as follows: 1. Noise reduction: The noise in the ECG samples is reduced. The frequencies between 4-48 Hz are only passed. This is done by using MATLAB filter design toolbox [4]. 273

3 Fig 4: System for modified Pan Tompkins algorithm Digital Band pass Filter Band pass filter for the QRS detection algorithm reduces noise in the ECG signal by matching the spectrum of the average QRS complex. Thus, attenuates noise due to muscle, 50 Hz interference, baseline wander, T wave interference. The pass band that maximizes the QRS energy is approximately in the 5Hz-15Hz range. The filter implemented in this algorithm is composed of cascaded high pass and low pass Butterworth IIR filters. A. Low-pass Filter For a Butterworth low pass filter of order N, the first 2N-1 derivatives of the squared magnitude response are zero at Ω = 0, where Ω represent the analog radian frequency. The Butterworth filter response is monotonic in the pass band as well as in the stop band. The basic Butterworth low pass filter function is given as H a (j Ω) 2 = 1/1+ (j Ω/j Ω c ) 2N Where H a is the frequency response of the analog filter and Ω c is the cutoff frequency in (radians/s). A Butterworth filter is completely specified by its cutoff frequency Ω c and order N. In this work the low pass filter is designed with following specifications: 8 th order Butterworth filter. Fs = 1000Hz. Fc = 40Hz. Attenuation: -3dB A. High-Pass Filter Similarly a high pass Butterworth filter can be designed with the help of MATLAB FDA toolbox. The Butterworth high pass filter may be specified directly in the discreet frequency domain as H (k) 2 = 1/1+ (Ω c / Ω) 2N, Where H (k) is the frequency response of the digital filter and Ω c is the cutoff frequency in (radians/s). In this work high pass filter is designed with following specifications: 8 th orde r Butterworth filter. Fs = 1000Hz. Fc = 4Hz. Attenuation: -3dB 1) Notch Filter Periodic interference may also be removed by notch filter with zeros on the unit circle in the Z-domain at the specific frequencies to be rejected. If f 0 is the interference frequency, the angles of the (complex conjugate) zeros required will be +/- (f 0 /fs)*2π; the radius of the zeros will unity. If harmonics are also present, multiple zeros will be required at +/- (nf 0 /fs)*2π, n representing the orders of all of the harmonics present. Here we have implemented the notch filter for the removal of power line interference and its harmonics (for India Power supply is 50Hz &USA is 60Hz). QRS Shape Classification The goal of QRS shape classification is to determine whether the QRS complex is normal or widened and bizarre shaped. Widened QRS complexes signify ventricular rhythms that may be life threatening. Now the R wave positions are determined after adaptive threshold is carried out, but the ECG signal which is used for adaptive threshold is distorted due to various signal processing tasks that are carried out initially. Hence the output of the Notch Filter is used again for adaptive threshold; this time the aim is to find the width of the QRS complex and not the R wave location. If the QRS width is greater than 40ms (at threshold level) then the QRS is termed as Ventricular [1]. The above method was successful for real ECG records of MIT/BIH Arrhythmia database however the results were not that encouraging with simulated data. So an alternative approach was used which is described below. A window containing 60 samples (60 milliseconds) before the peak of the QRS complex and 10 samples (10 milliseconds) after the peak has been used for calculating the AR coefficients. The order of the AR model used was 5. During the experiments, it showed up that the 6th AR coefficient was negative for wide-shaped or bizarre- shaped QRS complexes. This feature was used to classify the QRS complex as Ventricular. Algorithm implementation: As stated earlier modified Pan Tompkins [1] algorithm is used for the calculation of the parameters. The graphs in the fig 2 show step-by-step implementation of the algorithm. Following parameters are calculated, - Heart rate - PR interval - ST interval - No. Of R waves - P amplitude - T amplitude Calculation of Physiological Parameters: STEPS The first parameter detected is R amplitude by dividing the sampled signal in particular frames. The maximum amplitude in the time frame is R wave. Secondly the successive R-R distance is calculated. 274

4 Heart rate is calculated by knowing how many samples are there in successive R-R waves. The average heart is then calculated. The R wave is then deleted by knowing the standard R wave duration. The PR interval is calculated by searching previous samples from R waves. The average is then taken. The same procedure is repeated for ST interval. On the basis of the various physiological parameters extracted from ECG, arrhythmia is detected and the type diagnosed. The ECG can be online or offline. In this paper work is carried on the offline ECG data. The ECG samples from the MIT arrhythmia database [3] are taken for testing the software. Tests and Results: The software is tested against the various records numbered 105,106, 615,419, 217, 119, 800sv1, atf 6, atf 21 etc available on MIT BIH, Physionet database [3,5]. The results (Fig 5) i.e. the type of the arrhythmias detected by our software is checked against the information given in the MIT arrhythmia website. In most of the cases corrected results are observed. Arrhythmia detection: The next step is to detect the type of the arrhythmia [S2] present. There are universally accepted rules for different arrhythmias and their characteristic parameters. e.g. If heart rate > 90 Then arrhythmia is Tachycardia Depending upon the calculated parameters, the arrhythmia detection is done. At this stage the diagnosis being presented by our expert system is not yet good enough to be practically implemented in the hospitals. This is because we have only considered the five important ECG parameters for our analysis. We have not included the other physiological parameters to be considered before presenting a diagnosis. Moreover, we have grouped the arrhythmias under three heads. In our system, Sinus tachycardia, atrial tachycardia and junctional tachycardia are grouped under Supraventricular tachycardia. Premature a trial complexes and premature junctional complexes are grouped as Supraventricular premature complexes. The Sinus and junctional escape beats are diagnosed as Supraventricular Escape beats. GUI Building: While designing the GUI there are number of steps which are GUI Building: While designing the GUI there are number of steps which are taken into consideration. These steps are involved not only in the utility of the software to its full extent, but also the aesthetic look maintained by the GUI [4]. The GUI for this particular software is divided into number of subgroups according to their functionality. These groups are Parameter calculation window This parameter calculation window calculates and displays various time intervals of the patient s ECG waveform. This window provides the great flexibility that the user can calculate only the parameters he wants to observe by pressing particular push button. The number of parameters is as shown in figure 7. Reset push-button refreshes the screen for next parameter calculation. Help window (INFO DESK) This particular window provides the user a great database regarding the various kinds of arrhythmias & their corresponding treatment plants. In this window user only have to select the type of arrhythmia from popup menu whose information he want to know Fig 6. Signal filtering window This particular window shows the effect of processing on ECG waveform. The upper graph of the window shows raw ECG or the ECG directly extracted from the patient. The second graph displays the processed ECG signal. The filtering applied serves many purposes. Treatment window It recommends the treatment plan to the particular arrhythmia occurred. Preparing a full plan of treatment often requires more data which ECG doesn t provide, e.g. blood pressure, altered mental status, or symptoms like chest pain, etc. Moreover, ECG analysis system is meant to be a building block for a larger system that will deal with obtaining signs mentioned above. The result of the ECG analysis aims to be the input for diagnosis stage of the higher-level algorithm running the larger system. The robust treatment plan will be prepared by it. For the reasons mentioned above the advice part is rather small and simple. It presents a short treatment plan for the most dangerous, life-threatening arrhythmias ventricular fibrillation and ventricular tachycardia and those less dangerous, but still requiring immediate medical treatment bradycardia s (including second and third-degree AV block) and supraventricular tachycardia s. Real time database window 275

5 It is used to store the information of the patient under scan. helpful in remote areas where there are no medical professional in the immediate vicinity. Also with continuous recording of the ECG over long amount of time, one can predict the occurrence of fatal arrhythmia e.g. heart attack. ACKNOWLEDGEMENT This work is carried out in Instrumentation department of Vishwakarma Institute of Technology, Pune, and Maharashtra, India. Fig 6: Main GUI window. REFERENCES [1]William J. Tompkins, Biomedical Digital Signal Processing. [2] R. S. Khandpur, Handbook of biomedical. Instrumentation, Eighteenth reprints [3] [4] Merchand P., Graphics & GUI with MATLAB. [5] [6] Rangaraj M. R., Biomedical Signal Analysis. [7]Joseph Carr & John Brown, Introduction to Biomedical Equipment Technology. Fig 7: Parameter Calculation Window III.CONCLUSION AND FUTURE SCOPE In this work, an attempt is carried out for detection of ECG feature extraction. Pan Tompkins algorithms steps are modified for detection of ECG arrhythmias. Results are encouraging. With the help of this software we can develop an expert system, which is reliable and cheap in cost. This is quite 276