ECG Generation using AFG with Arrhythmia Detection and Analysis Dattatray Sawant & Y. S. Rao Sardar Patel Institute of Technology,Mumbai-400058, India E-mail : dssawant1@gmail.com, ysrao@spit.ac.in Abstract Electrocardiograms (ECG) are electrical views of the heart, recorded by placing electrodes on the patient s body. The ECG contains a wealth of diagnostic information routinely used to guide clinical decision making in hospitalized patients. This paper proposes an analysis of the ECG signal and further detecting the arrhythmia if the ECG signal is abnormal. Thus, we tend to develop an algorithm which broadly classifies different arrhythmias. Thus, saving time required for diagnosis and enabling faster medication. Its verification is done using ARM LPC2148 and AFG3102. Keywords ARM, Arbitary Function Generator (AFG), Electrocardiogram (ECG), Reduced Instruction Set Computer (RISC). I. INTRODUCTION An arrhythmia is a problem with the rate or rhythm of the heartbeat. During an arrhythmia, the heart can beat too fast, too slow, or with an irregular rhythm. The heart may not be able to pump enough blood to the body. Lack of blood flow can damage the brain, heart, and other organs. Electrocardiograms (ECG) are electrical views of the heart, recorded by placing electrodes on the patients body. ECGs provide the cardiologist TIME INTERVALS OF VARIOUS ARRHYTHMIAS in ms with useful information about the rhythm and functioning of the heart. Further ECG signal is divided into parameters like PR intervals, P wave, R-R interval, -PR segment, QRS complex, S-T segment, T wave, S-T interval and Q-T interval. Thus one can detect various arrhythmias by simply analyzing an ECG waveform and various parameters. Importance of Arrhythmia detection Many arrhythmias occur in people who do not have underlying heart diseases. They do not need extensive exams or special treatments for their condition. In some people, arrhythmias are associated with heart disease. In such cases, heart disease, not the arrhythmia, poses the greatest risk to the patient. In a very small number of people with serious symptoms, arrhythmias themselves are dangerous. These arrhythmias require medical treatment to keep the heartbeat regular. Therefore in such cases accurate detection of arrhythmias at an early stage is essential which can be done by analyzing ECG waveform. Electrocardiogram The electrocardiogram (EKG or ECG) shown in the Fig.1 is [3] a diagnostic tool that measures and records the electrical activity of the heart in exquisite detail. Duration of the QRS complex is a key characteristic of ECG signals used in analysis and classification. It s duration is approximately 100ms [4] in the human heartbeat. The ECG signal is recurrent approximately every 800ms in healthy humans but varies between subjects. Fig. 1 : Typical ECG waveform 68
TABLE I TIME INTERVAL OF VARIOUS ARRHYTHMIAS IN MS II. ARBITARY FUNCTION GENERATOR Obtaining ECG waveforms of various arrhythmias was difficult. Hence using AFG3102, waveforms for various arrhythmias were created. Using Arb Express, a PC software, waveforms of various arrhythmias can be seamlessly exported to AFG3102 oscilloscope and a corresponding real time signal is generated. The waveforms which have been created using ArbExpress are Bradycardia, Tachycardia, First degree AV Block, Second degree AV block Type 1 and Type 2, Atrial Flutter, SupraVentricluar Tachycardia, Myocardial Infarction (Heart attack), Atrial fibrillation and Ventricular fibrillation. The created waveforms are converted into real time signals using AFG3102. Amplitude and frequency can be varied using AFG3102. An offset can also be added if required. The AFG3102 delivers up to 10 Vpp into 50 Ohm loads, reducing the need for an additional amplifier to drive power semiconductors in automotive applications and other devices in science and industry, simplifying the test setup and reducing the equipment cost. On pulse waveforms, leading and trailing edge time can be set independently. External signals can be connected and added to the output signal. Dual channel models can generate two identical or completely different signals. A large screen shows all relevant waveform parameters and graphical waveshape at a single glance. This real time waveform obtained using AFG3102 can be displayed using a Digital Storage Oscilloscope (DSO). Also the corresponding.csv and.xls file can be obtained from DSO. These files will contain samples of the displayed waveform. III. ARM ARM is a 32-bit reduced instruction set computer (RISC) instruction set architecture (ISA) developed by ARM Holdings. It was named the Advanced RISC Machine and, before that, the Acorn RISC Machine. The relative simplicity of ARM processors makes them suitable for low power applications. The ARM architecture is the most widely used 32-bit instruction set architecture in numbers produced. Thus the analysis of the ECG signal is done using ARM processor. The ECG signal is received on the ADC pin of ARM controller. The signal is processed by ARM and various parameters like QRS interval, R-R interval, P-R interval, S-T interval and P, Q, R, S and T location are obtained. And by comparing these parameters with normal ECG Parameters using ARM,various arrhythmias can be detected and displayed on LCD.The reconstruction of the signal is done through MATLAB. A. ArbExpress IV. SOFTWARES ArbExpress AXW100 Waveform Creation and Editing Tool for Tektronix AWG/AFG is PC-based software that runs on the Windows 98, Windows NT, Windows 2000, Windows XP Professional, and Windows Me operating systems. ArbExpress generates waveforms for Tektronix signal sources instruments. One can create and edit waveforms: transfer waveforms to and from Tektronix oscilloscopes, Arbitrary Waveform Generators (AWG), and Arbitrary Function Generators (AFG): and remotely control AWGs and AFGs. ArbExpress generates the following standard waveforms: - Sine, Square, Triangle, Pulse, DC, Exponential Rise, Exponential Decay, Noise, Sinc, Sweep, Multi-Tone, and Lorentz. One can generate arbitrary waveform using table editor where based on values provided by user at various time interval, waveform will interpolated in smooth/linear fashion. B. Keil Keil for ARM is a C/C++ Compilation Toolchain. It consists of uvision4 IDE, debugger, and simulation environment. These development tools for the ARM family of microcontrollers allow to write ARM applications in C or C++ that, once compiled, have the efficiency and speed of assembly language. The ARM Compiler toolchain translates C/C++ source files into relocatable object modules which contain full symbolic information for debugging with the uvision Debugger or an in-circuit emulator. In addition to the object file, the compiler generates a listing file which may optionally include symbol table and cross-reference information. It is,thus, used to configure the ARM with the code for arrhythmia detection. C. MATLAB MATLAB is a programming environment for algorithm development, data analysis, visualization, and numerical computation. Using MATLAB, we can solve 69
technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. It allows one to perform numerical calculations, and visualize the results without the need for complicated and time consuming programming. MATLAB allows its users to accurately solve problems, produce graphics easily and produce code efficiently. V. RESULTS AND ANALYSIS A. Sinus Bradycardia An excessive heart rate above 100 beats per minute (BPM) which originates from the SA node. Causes include stress, fright, illness and exercise. Not usually a surprise if it is triggered in response to regulatory changes e.g. shock. But if their is no apparent trigger then medications may be required to suppress the rhythm shown in Fig. 3. From the Fig. 3, we observed that : b) Rate -More than 100 beats per minute d) P Wave -Visible before each QRS complex e) P-R Interval Normal C. Atrial Flutter From the Fig. 4, we observed that : b) Rate -Around 110 beats per minute c) QRS Duration -Usually Normal d) P Wave -Replaced with multiple F (flutter) waves, usually at a ratio of 2:1 (2F -1QRS) but sometimes 3:1 e) P-R Interval Not measurable Fig. 2 : Plot of Sinus Bradycardia A heart rate less than 60 beats per minute (BPM). This in a healthy athletic person may be normal, but other causes may be due to increased vagal tone from drug abuse, hypoglycaemia and brain injury with increase intracranial pressure (ICP) as shown in Fig. 2. From the Fig.2, we observed that : b) Rate -less than 60 beats per minute d) P Wave -Visible before each QRS complex e) P-R Interval Normal B. Sinus Tachycardia D. Atrial Fibrillation Fig. 4: Plot of Atrial Flutter Fig. 3: Plot of Sinus Tachycardia Fig. 5: Plot of Atrial Fibrillation 70
Many sites within the atria are generating their own electrical impulses, leading to irregular conduction of impulses to the ventricles that generate the heartbeat. This irregular rhythm can be felt when palpating a pulse. From the Fig. 5, we observed that : a) Rhythm -Irregularly irregular b) Rate -Usually 100-160 beats per minute but slower if on medication d) P Wave -Not distinguishable as the atria are firing off all over e) P-R Interval -Not measurable E. 1st Degree AV Block 1st Degree AV block is caused by a conduction delay through the AV node but all electrical signals reach the ventricles. This rarely causes any problems by itself and often trained athletes can be seen to have it. The normal 4 P-R interval is between 0.12s to 0.20s in length, or 3-5 small squares on the ECG. From the Fig. 6, we observed that : b) Rate Regular d) P Wave -Ratio 1:1 e) P Wave rate Normal f) R interval- Prolonged Fig. 6 : Plot of 1st Degree AV Block F. 2nd Degree Block Type 1 Fig. 7: 2nd Degree Block Type 1 Another condition whereby a conduction block of some, but not all atrial beats getting through to the ventricles. There is progressive lengthening of the PR interval and then failure of conduction of an atrial beat, this is seen by a dropped QRS complex. From the Fig. 7, we observed that: a) Rhythm -Regularly irregular b) Rate -Normal or Slow d) P Wave -Ratio 1:1 for 2,3 or 4 cycles then 1:0 e) P Wave rate -Normal but faster than QRS rate f) R Interval -Progressive lengthening of P-R interval until a QRS complex is dropped G. 2nd Degree Block Type 2 When electrical excitation sometimes fails to pass through the A-V node, this intermittent occurrence is said to be called second degree heart block. Electrical conduction usually has a constant P-R interval, in the case of type 2 block atrial contractions are not regularly followed by ventricular contraction. From the Fig. 8, we observed that : b) Rate -Normal or Slow c) QRS Duration Prolonged d) P Wave -Ratio 2:1,3:1 e) P Wave rate -Normal but faster than QRS rate f) R Interval -Normal or prolonged but constant 71
developed to detect and analyze various arrhythmias. The reconstruction of the signal is done through MATLAB and the parameters are verified by comparing the results. VII. ACKNOWLEDGMENT The authors thankful to the Sardar Patel Institute of Technology, India for providing the necessary facilities for carrying out this work. Fig. 8: 2nd Degree Block Type 2 H. Myocardial Infarction Fig. 9 : Myocardial Infarction It is another name for heart attack From the Fig. 9, we observed that : b) Rate -80 Beats per minute d) P Wave Normal e) S-T Element does not go isoelectric which indicatesinfarction VI. CONCLUSION The ECG remains the gold standard for diagnosis of heart diseases, in spite of the advancements of many other diagnostic techniques. We have successfully calculated all the parameters of the ECG. With the knowledge of these parameters, we have detected various arrhythmias. Hence, a diagnostic tool is VIII. REFERENCES [1] Tanis Mar,Sebastian Zaunder, Jaun Pablo Martinez, Mariano Llamedo, and Rudiger Poll, Optimization of ECG Classification by means of Feature Selection, IEEE Transactions on Biomedical Engineering, vol. 58, no. 8, pp. 2168-2177, 2011. [2] Walter G. Besio, Asha K Kota, Automated Laplacian ECG Moment of Activation determination Algorithm during pacing, Proceedings on 31 st International Congress on Electrocardiography, Advance in Electrocardiology, 2004. [3] Abdallah, A. Haga and K. Kuroda, An Efficient Algorithm and Embedded Multi- core Implementation of ECG Analysis in Multi-lead Electrocardiogram Record, 39th International Conference on Parallel Processing Workshops(ICPPW), Sept. 2010. [4] C.Meyer, J.F.Gavela, M.Harris Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals, IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 3, July 2006. [5] Suppappola, S., et al. Nonlinear transforms of ECG signals for digital QRS detection:a quantitative analysis., IEEE Transactions on Biomedical Engineering,April 1994. [6] K. Waseem, A. Javed, R. Ramzan and M. Farooq, Using Evolutionary Algorithms for ECG Arrhythmia Detection and Classification, 7th International Conference on Natural Computation, 2011. 72