DEVELOPMENT OF A SIMPLE SOFTWARE TOOL TO DETECT THE QRS COMPLEX FROM THE ECG SIGNAL
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1 DEVELOPMENT OF A SIMPLE SOFTWARE TOOL TO DETECT THE QRS COMPLEX FROM THE ECG SIGNAL Michaella Ignatia Tanoeihusada 1), Wahju Sediono 2) Swiss German University, Tangerang 1), Agency for the Assessment and Application of Technology, Jakarta 2) nightingale87@gmail.com 1) ABSTRACT Tujuan utama dari paper ini adalah untuk mendisain suatu software yang dapat digunakan dalam menentukan kompleks QRS yang terdapat di dalam setiap signal EKG. Elektrokardiogram (EKG) adalah alat untuk mengukur dan merekam aktivitas elektrik jantung. EKG mempunyai peran penting dalam mendiagnosis kondisi jantung yang mengindikasikan penyakit jantung yang dapat menyebabkan kematian. Dalam paper ini penentuan kompleks QRS dari signal EKG dilakukan berdasarkan analisa kemiringan grafik signal EKG, besarnya amplitudo, dan lebar kompleks QRS itu sendiri. Dengan mengaplikasikan metode ini, detak jantung dapat dihitung. Kemudian, dengan tambahan suatu metode sederhana dapat dilakukan pengklasifikasian atas kondisi jantung seorang pasien. Karena kesederhanannya software ini dapat digunakan untuk membantu mahasiswa dalam memahami signal Elektrokardiogram secara lebih baik. Kata Kunci: Elektrokardiogram, Kompleks QRS 1. Introduction Heart disease is the single biggest killer disease in the world [3]. The number of death caused by heart disease has been increasing year by year. In Indonesia, the number of fatalities caused by this disease has been increasing since 1995 [2]. A way to diagnose our heart condition is conducted through the Electrocardiogram (ECG) analysis. The electrocardiogram (ECG) is a diagnostic tool that measures and records the electrical activity of the heart in exquisite detail. It is done to evaluate signs and symptoms that could indicate heart problems. Interpretation of these details allows diagnosis of a wide range of heart conditions. These conditions can vary from minor to life threatening. In a typical ECG record, three clearly recognizable waves appear with each heartbeat. The first one is called P wave, the second is called QRS complex, and the third wave is called T wave. The detection of QRS complex from an ECG is important, because the R peak in the QRS complex is very high compared to the others. Thus, the value of R peak can be used as a parameter to decide whether the patient is suffering from heart disease or not. Within the ECG signal a QRS complex begins as a downward deflection, continues as a large, upright, triangular wave, and ends as a downward wave. The QRS complex represents rapid ventricular depolarization, as the action potential spreads through ventricular contractile fibers. It is periodic due to the heart's frequency, which is also periodic [4]. A typical ECG signal for a normal patient is shown in Figure 1. Figure 1. Electrocardiogram (ECG) For One Heartbeat [6] The primary purpose of this work is to design a software tool that will assist in determining the QRS waveform from the ECG signal. The recognition of the QRS complex is based on the analysis of the signal slope, amplitude, and its width. By applying this recognition method, the heart rate and condition of the heart can be specified. Later this tool can be used by medical students or other students in the related field (e.g. biomedical students) to assist them in analyzing and having a better understanding about ECG signal. 2. Methodology To detect a QRS complex, we need to perform a series of steps consisting of band-pass filtering, deriving, squaring, integrating and thresholding as shown in Figure
2 ECG y[n] z[n] d[n] s[n] w[n] LOW-PASS FILTER HIGH-PASS FILTER DERIVATIVE SQUARING MOVING WINDOW INTEGRATION THRESHOLD t[n] Figure 2. Detection of QRS Complex In the first step we conduct a band-pass filtering. This band-pass filter, composed of a low-pass followed by a high-pass integer filter, is needed to increase the signal-to-noise ratio or to attenuate the noises coming from muscle noise, artifacts due to electrode motion, power-line interferences, baseline wander, P waves, and T waves with high frequency characteristics similar to QRS complex [1][5][6]. The result of such band-pass filter is shown in Figure 3. Figure 3. ECG Signal After Band-Pass Filtering Amplitude (mv) Squaring time (s) Figure 4. ECG Signal After Differentiation After band-pass filtering, the signal is differentiated in order to find the highest slopes that distinguish QRS complexes significantly from the other parts of the ECG waves (Fig. 4). This step is then followed by squaring data point by point to make the entire signals positive (Fig. 5). The squaring function will non-linearly amplify the output of the derivative process, and also emphasize the higher frequencies in the signal due to the QRS complex. Figure 5. ECG Signal After Squaring After squaring the derivative signal, the moving window integral is performed to obtain waveform feature information, in addition to the slope of the R wave. This step is quite important since many abnormal QRS complex with large amplitudes and long durations might not be detected using differentiation only. Following the squaring step we need to perform a moving window integral (Fig. 6). Moving Window Integral 150 Amplitude (mv) time (s) Figure 6. ECG Signal After Moving Window Integral The number of samples in the moving window plays an important role. The width of the window should be approximately the same as the widest possible QRS complex. If the size of the window is too wide, the integration waveform will merge the QRS and T complexes together. If it is too narrow, some QRS complexes will produce several peaks in the integration waveform. These can cause difficulty in the subsequent QRS detection processes. The width of the window is determined experimentally. For a sample rate 200 samples/s, the window is 30 samples wide (150 ms) [6]. 193
3 The next processing step was completed by conducting the threshold operation, which eliminates remaining noise parts. This approach reduces the number of false positive caused by types of noise that mimic the characteristics of the QRS complex. Determination of R wave location was done by searching the highest amplitude of the processed data resulted from the previous steps. This step is implemented by locating the corner points separate R waves from other signal parts. After the initial maximum point location was marked, the next step is to mark its final point. It is done by searching the final highest point in the R-wave region between the corner points. By localizing all R peaks of the ECG signal, the R-R interval, thus the heart rate, can be calculated. Especially, heart rate can be calculated through the following steps: 1. Calculating R-R intervals. 2. Deriving the heart rate from the R-R interval. 3. Calculating the average of heart beat by summing the heart rate then dividing it by the number of R peaks. 4. Determining heart beat per minute by multiplying the average of heart rate with 60 seconds. Furthermore, based on the calculated heart beat per minute, classification of disease can be divided into either bradycardia which is a condition in which the heart rate is slower than normal (less than 60 bpm), or tachycardia in which the heart rate is faster than normal heart rate (100 bpm or more) [4]. The result of classification of heart rate is shown in Table 1. Table 1. Heart Rate Classification Heart Rate heart rate < 60 bpm heart rate > 100 bpm 80 bpm < heart rate 100 bpm 60 bpm heart rate 80 bpm Classifications but risk of 3. Result and Discussion Table 2 shows result of the calculation performed using the developed software tool. In this experiment the data were obtained from MIT/BIH arrhythmia database. Our result shows that the database can be classified into 3 categories with an accuracy of 90.6%. There are 4 records which indicate poor accuracy: MIT_104 (accuracy: 32.7%); MIT_203 (accuracy: 30.9%), MIT_207 (accuracy: 18.3%), and MIT_228 (accuracy: 32%). In these 4 records the false detection of the QRS is caused by the very big noise that is similar to the QRS complex. Especially, in MIT_104 (Fig. 7), MIT_207 (Fig. 8), and MIT_228 (Fig. 9) the failure caused by the T wave is very big which makes it mimic the QRS complex. The phenomena lead to a false QRS complex, instead of a T wave, detection. Figure 7. ECG Signal of MIT_104 Figure 8. ECG Signal of MIT_
4 Figure 9. ECG Signal of MIT_228 Figure 10. ECG Signal of MIT_203 In the MIT_203 (Fig. 10) the failure is initiated by very big muscle noise caused by movement of the patient that distorts significantly the recording of the ECG signal. Despite of the above deviations, these results confirm the feasibility of this software as a simple means for medical or biomedical students in diagnosing the ECG signal. Table 2. Result of Experiment Data HR1 (bpm) SD Diagnose HR2 (bpm) Accuracy * MIT_100 MIT_101 MIT_102 MIT_103 MIT_104 MIT_105 MIT_106 MIT_107 MIT_108 MIT_109 MIT_111 MIT_112 MIT_113 MIT_114 MIT_115 MIT_116 MIT_117 MIT_118 MIT_119 MIT_121 MIT_122 MIT_123 MIT_124 MIT_ but risk of but risk of but risk of but risk of but risk of but risk of % 98.1% 98.5% 92.2% 32.7% 96.6% 98.9% 83.3% 98.5% 96.4% 97.8% 91.6% 99.5% 90.2% 98.2% 91.5% 94.1% 99.9% 90% 195
5 Data HR1 (bpm) SD Diagnose HR2 (bpm) Accuracy * MIT_201 MIT_202 MIT_203 MIT_205 MIT_207 MIT_208 MIT_209 MIT_210 MIT_212 MIT_213 MIT_214 MIT_215 MIT_217 MIT_219 MIT_220 MIT_221 MIT_222 MIT_223 MIT_228 MIT_230 MIT_231 MIT_232 MIT_233 MIT_ but risk of but risk of but risk of but risk of but risk of but risk of but risk of Average HR1: Heart Rate which is measured by using this software. HR2: Heart Rate which is calculated from the MIT/BIH arrhythmia database signal. Accuracy * : 1 (HR2-HR1 / HR2) x 100% % 30.9% 97.5% 18.3% 86.9% 93.7% 95.6% 98.7% 98.9% 99.9% 97.2% 100% 95.9% 98.3% 95% 98% 100% 32% 95.1% 96.1% 94% 90% 90.6% 4. Conclusion A new software tool is proposed to help in diagnosing the ECG signal. Especially, using this software we can detect the QRS complex from the ECG signal. This QRS detection can be used as a basic means in determining the heart rate of the patient. A further simple classification method enables us to categorize the heart condition into several cases with risks of heart diseases. To get a more accurate and comprehensive diagnosis we should always take a look at the history of the patients beforehand. Nevertheless, despite its simplicity, this software tool can help medical or biomedical students to get a better understanding about the principle of the ECG signal. References [1] Hamilton, P. S. & Tompkins, W. J. (1986). Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database. IEEE Transaction on Biomedical Engineering, vol. BME-33, no. 12, pp [2] accessed June 11, 2008, 10.00pm. [3] accessed June 11, 2008, 10.00pm. [4] Martini, F.H. and Bartholomew, E.F. (2002). Essential of Anatomy & Phisiology. 4 th ed. San Fransisco: Pearson. [5] Pan, J. & Tompkins, W. J. (1985). A Real-Time QRS Detection Algorithm. IEEE Trasnsactions on Biomedical Engineering, vol. BME-32, no. 3, pp [6] Tompkins, W. J. (eds). (1993). Biomedical Digital Signal Processing. New Jersey: Prentice Hall. 196
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