CC BY
79
Computer modeling electrocardiogram signals using notch filters
1 2
Altay Y. , Makesheva K. (Russian Federation, Republic of Kazakhstan) Компьютерное моделирование сигнала электрокардиограммы с помощью
режекторного фильтра
12
Алтай Е. А. , Макешева К. К. (Российская Федерация, Республика Казахстан)
1Алтай Ельдос Алтайулы / Altay Yeldos - магистрант, кафедра систем управления и информатики, Санкт-Петербургский национальный исследовательский университет информационных технологий, механики оптики,
г. Санкт-Петербург;
2Макешева Куляш Кунуспаевна /Makesheva Kuliash - кандидат физико-математических наук, доцент, кафедра
робототехники и технических средств автоматики, Казахский национальный технический университет имени К. И. Сатпаева, г. Алматы
Abstract: the proposed method is to study and analyze electrocardiograph (ECG) waveform to detect abnormalities present with reference to P, Q, R and Speaks. Proper utilization of MATLAB functions (both built-in and user defined) can lead us to work with ECG signals for processing and analysis in real time applications. The simulation would help in improving the accuracy and the hardware could be built conveniently. Аннотация: предлагаемый метод состоит в изучении и анализе формы сигнала электрокардиографа (ЭКГ) для обнаружения имеющихся аномалий относительно пиков P, Q, R и S. Правильное использование функций MATLAB (как встроенных, так и определенных пользователем) может позволить выполнять работы с сигналами ЭКГ для обработки и анализа в приложениях в реальном времени. Моделирование поможет повысить точность для удобного конструирования аппаратных средств.
Keywords: computer modeling, electrocardiogram, notch filters, MATLAB.
Ключевые слова: компьютерное моделирование, электрокардиограмма, режекторный фильтр, MATLAB.
Electrocardiogram (ECG or EKG) is a diagnostic tool that measure and records the electrical activity of the heart in exquisite detail. Interpretation of these details allows diagnosis of wide range of life threatening heart conditions as referred from [1] and [2]. The current is diffused around the surface of the body. An ECG is generated by a nerve impulse stimulus to the heart. The current at the body surface will build a voltage drop, which is a couple of microvolt to mill volt with an impulse variation. This is very small amplitude of impulse, which requires a couple of thousand times amplification. A typical ECG tracing of a normal heart rate (or cardiac cycle) consists of a P wave, QRS complex and a T wave. A small U wave is normally visible in 50 to 75% of ECGs. The baseline voltage of ECG is known as isoelectric line. Typically, the isoelectric line is measured as the portion of tracing following the T wave and preceding the next P wave. Electrical activity of the heart can be recorded at the surface of the body using an electrocardiogram. Therefore, the electro-cardio-gram (EKG) is simply a voltmeter that uses up to 12 different leads (electrodes) placed on designated areas of the body. Figure 1 shows typical ECG trace as referred [2]. The electrical activity of the heart is generally sensed by monitoring electrodes placed on the skin surface. The electrical signal is very small (0.0001 to 0.003). These signals are within frequency range of 0.05 to 100Hz (Hertz) or cycle per second. In ECG signal processing, instrumentation amplifier plays major role since signal generated by human body are very low in amplitude. High gain must be obtained with high common-mode rejection ratio (CMRR). ECG signals are very noisy, usually 50Hz. MATLAB was used to test and adjust a digital filter as referred from [3], in order to obtain a good QRS complex noise free, which represents the ventricular depolarization in the ECGs, i.e., it shows the electrical impulse of the heart as it passes through the ventricles.
Figure1. Typical one-cycle ECG signal tracking
The ECG signal is generated by the MATLAB code from real time data. The objective is to produce the typical ECG waveforms of different leads and as many arrhythmias as possible. This technique has many advantages in the simulation of ECG waveforms. Firstly saving time, secondly removing noise and thirdly Q,R,S detection in an easy manner.
Significant Features of ECG Waveform
A typical scalar cardiogram lead is shown in Figure 1, the significant features of waveform are the P, Q, R, S waves, the duration of each wave and time intervals such as P-R, S-T and Q-T intervals. ECG signal is periodic with fundamental frequency determined by the heartbeat. It also satisfies the Dirichlet's condition. Hence, Fourier series can be used to represent an ECG signal. If we observe figure 1 carefully, we may notice that a single period of an ECG signal is a mixture of triangular and sinusoidal waveforms. The significant feature of ECG signal can be represented by shifted and scaled versions. Such one waveform is shown in figure 3.
• QRS, Q and S portion of ECG signal can be represented by triangular waveforms.
• OP, T and U portions can be represented by triangular waveforms.
Figure 2. Typical ECG waveform in MATLAB
The generated output signal by MATLAB is shown in figure 2. The specifications are default for this signal which can be changed according to the user's requirement while simulating the MATLAB code.
Generally, the recorded signal is often contaminated by noise and artifacts that can be within the frequency band of interest and manifest with similar characteristic as the ECG signal itself. In order to extract noisy ECG signals, we need to process the basic ECG signal.
ECG signal processing can be roughly divided in to two stages:
1) Preprocessing
2) Feature extraction.
The preprocessing stage removes or suppresses noise from the raw ECG signal. The feature extraction stage extracts diagnostic information from the ECG signal referred as from [4]. Preprocessing ECG signals help us remove contaminants from ECG signals. ECG contaminants can be classified into the following categories referred as from [5]:
• Power line interference
• Electrode pop or contact noise
• Patient-electrode motion artifacts
• Electromyography (EMG) noise
• Baseline wandering
Among these noise, the power line interference and the baseline wandering are the most significant and they can strongly affect ECG signal analysis. Except for these two noises, other noises may be wideband and usually complex stochastic process which also distort the ECG signal. The power line interference is narrow-band noise centered at 50 Hz with a bandwidth of equal or less than 1 Hz.
Usually the ECG signal acquisition hardware can remove the power line interference. However, the baseline wandering and other wideband noises are not easy to be suppressed by hardware equipments. Instead, the software scheme is more powerful and feasible for offline ECG signal processing. We can use the following MATLAB code method to remove the noise. Figure 3 represent the filtered output of noisy ECG signal.
Figure 3. Represent the filtered output of noisy ECG signals
CONCLUSION
MATLAB has immense effect on ECG signal processing. It is so useful and handy that everyone can monitor his/her heart condition simply utilizing the power of MATLAB. The above discussed examples and techniques can be utilized for real time experimental/lab purpose. One of the crucial steps in the ECG analysis is to accurately detect the different waves namely P, Q, R and S depicting the entire cardiac cycle. The methodology is definitely a new approach to detect points and nonstandard shapes present in the ECG signals. This proposed wok could be continued to further improve the algorithm to detect abnormalities and implement this system to find deposits of arrhythmia in the heart by using calculations of intervals between impulses of two different signals in real time. For processing and design of detection algorithms, MATLAB is used, in which they were implemented as mathematical signal processing operations and statistical analysis of test results.
References
1. Xu Xianhong. Research and design of 12-Lead Synchronization ECG Signal Detection and Analysis System. // International journal of medical, pharmaceutical science and engineering. - 2011. - № 5. - P. 257-264.
2. Sun Wenzuo Research of the 12-lead ECG signal acquisition and analysis system. // Wseas transactions on signal processing. - 2011. - № 4. - P. 151-159.
3. Yihang Zhang, Guangmin Sun. 12 lead ECG data acquisition system based on ADS 1298 / Procedia engineering. - 2012. - № 29. - P. 2103-2108.
4. Neeraj Kumar, Imteyaz Ahmad. Signal processing of ECG using Matlab. // International journal of scientific and research publications. - 2012. - № 10. - P. 1-6.
5. Noureddine Belgacem. Mobile personal electrocardiogram monitoring system with patient location. // Journal of biomedical engineering. - 2009. - № 4. - P. 69-71.
6. Kening Wang, Weizhao Zhang. Design of ECG Signal Acquisition System Based on DSP // International Workshop on Information and Electronics Engineering. - 2012. - №5. - P.3763-3767
