Artificial Neural Networks in Cardiology - ECG Wave Analysis and Diagnosis Using Backpropagation Neural Networks

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1 Artificial Neural Networks in Cardiology - ECG Wave Analysis and Diagnosis Using Backpropagation Neural Networks 1.Syed Khursheed ul Hasnain C Eng MIEE National University of Sciences & Technology, Pakistan Navy Engineering College, Habib Rehmatullah Road, Karachi, Pakistan super.net.pk 2.Syed Muhammad Asim Biomedical Engineering Dept., Sir Syed University of Engineering & Technology, University Road, Karachi, Pakistan yahoo.com Abstract: Artificial Neural Networks are being increasingly used in medical applications such as Cardiology. Backpropagation neural network has been used because of its good pattern recognition capabilities in supervised training mode for ECG wave analysis and diagnosis. The developed system was part of the Computerized Patient Monitoring System, developed as a final year project, and analyzes ECG waves in both time and frequency domain with real time processing capabilities. The network does dynamic studies of the wave with point-to-point calculations and 2D wave pattern recognition. The system also has learning abilities for learning custom ECG patterns and their related abnormalities, and this can be used to store upto 25 reference abnormal ECG patterns to be used for future diagnostic calculations. The system learns in both on and off line modes and thus produces results in real time. The data acquired by the system can be used with a database management system so that it can be viewed anywhere in the hospital using hospital s LAN. Furthermore the data can also be communicated on the Internet. Introduction An artificial neural network (ANN) is an information-processing paradigm, implemented in hardware or software that is modeled after the biological processes of the brain. An ANN is made up of a collection of highly interconnected nodes, called neurons or processing elements. A node receives weighted inputs from other nodes, sums these inputs, and propagates this sum through a Input Adjust weight Neural Network including connections ( called weights) between neurons Output Target Fig 1. Block diagram of the neural network function to other nodes. This process is analogous to the actions of a biological neuron. The network obtains the final result once the weight & biases are adjusted as shown in Fig.1. [1] ANNs most important advantage is that they can be used to solve problems of considerable complexity; problems that do not have an algorithmic solution or for which such a solution is too complex to be found. Because of their abstraction from the brain, ANNs are good at solving problems that humans are good at solving but which computers are not. Pattern recognition and classification are examples of problems that are well suited for ANN application. [2]. Neural Networks: The approach of using a neural network based system for biomedical signal analysis and diagnosis gives strength to the idea of automated signal interpretation. This automation process reduces the burden on the doctors/paramedics involved in critical care situations like ICUs and CCUs and with the option of both time and amplitude domain analysis the diagnosis can be made with utmost precision. Once a computer system is able to diagnose complex disease patterns from measured signals and data stored in electronic patient records, it is in a position to assist clinicians in making therapeutic decisions. As more and more hospitals take advantage of the power of computers and begin to store medical data in digital formats, computer based technologies, such as artificial neural networks, can provide useful aids to assist the physician in the diagnosis of many diseases. This tool could be used in personal health diagnostic systems for continuous diagnosis of health and for periodic clinical tests: graded exercise tests and cardiovascular stress tests. For example, a real-time diagnostic system incorporating the cardiovascular models may be used to monitor the health of workers in hazardous environments or to monitor and control administration of medication for hospital patients. Backpropagation Neural Networks: The backpropagation neural network is a feed-forward

2 network that usually has hidden layers, as shown in Fig.2.[3]. The activation function for this type of network is generally the sigmoid function. Since the activation function for these nodes is the sigmoid function above, the output from each node is given by where a i is the total input to node i, which is given by Note how the weights are indexed. Weight w ij is the weight of the connection from node j to node i. Now, as for the perceptron, we will minimize the error in the network by using the gradient descent algorithm to adjust the weights. So the change in the weight from node j to node i is given by where E k is the mean square error for the k th pattern, The error for a hidden node i is calculated from the errors of the nodes in the next layer to which node i is connected. This is how the error of the network is back propagated. So, putting it all together, the change for weight w ij, where node i is in a hidden layer, is given by The changes in the weights of the network, which allow the network to learn, are now totally defined. This generalized delta rule for backpropagation neural networks defines how the weights between the output layer and the hidden layer change, and how the weights between other layers change also. This network is called backpropagation because the errors in the network are fed backward, or backpropagated, through the network. Fig 2: Backpropagation network Generalization is perhaps the most useful feature of a backpropagation network. Since the network uses supervised training, a set of input patterns can be organized into groups and fed to the network. The network will observe the patterns in each group, and will learn to identify the characteristics that separate the groups. Often, these characteristics are such that a trained network will still be able to classify new, or unseen, input patterns into the correct groups, even if the patterns are noisy. The network learns to ignore the irrelevant data in the input patterns. Electrocardiography: The electrocardiogram (ECG) is a graphic recording of the electrical potentials produced in association with the heartbeat. The impulses produced by the SA node results in excitation of the muscle fibers throughout the myocardium. Impulse formation and conduction produce weak electrical currents that spread through the entire body. By applying electrodes to various positions on the body that are the right arm(ra), left arm(la), right leg(rl) and left leg(ll), and connecting these electrodes to an electrocardiographic apparatus, the ECG is recorded as shown in Fig.3. NORMAL ELECTROCARDIOGRAPHIC COMPLEXES: P wave: The deflection produced by arterial depolarization. Ta wave: The deflection produced by artrial repolarization. Q(q)wave: The initial negative deflection resulting from ventricular depolarization. R(r) wave: The first positive deflection during ventricular depolarization. S(s) wave: The first negative deflection of ventricular depolarization that follows the first positive deflection(r). R': The second positive deflection, i.e., the first positive deflection during ventricular depolarization that follows the S wave. The negative deflection following the r' is termed the s'. T wave: The deflection produced by ventricular repolarization.

3 U wave: A deflection (usually positive) seen following the T wave and preceding the next P wave. [4]. Fig.3. Diagram of electrocardiographic complexes, intervals and segments Normal Interval Values Interval R-R Interval P-P Interval P-R Interval QRS Interval Ventricular Activation Time (VAT) Q-T Interval Duration Varies Varies sec 0.1sec 0.03sec (in V 1-2 ) 0.05sec (in V 5-6 ) 0.42 (in men) 0.43 (in women) Normal Segments and Junctions:- PR segment: That portion of the ECG tracing from the end of the P wave to the onset of the QRS complex. RS-T junction (J): The point at which the QRS complex ends and the RS-T segment begins. RS-T segment or ST segment: The portion of the tracing from the J to the onset of the T wave. The automated signal interpretation The motivations for automating signal interpretation are numerous and are not unique to medicine. The most pressing arise from the difficulties clinicians face when they continuously monitor patient data. These human factors include the problems of data overload, varying expertise, and human error. It comes as no surprise that clinicians may have difficulty in interpreting information presented to them on current monitoring systems. Not only may the amount of information available be greater than can be assimilated, but the clinical environment provides distractions with other tasks, reducing the effort that can be devoted to signal interpretation. Worse still, current monitors flood clinicians with false alarms, providing further unnecessary distraction. It is also clear that the level of expertise requires consultation with more experienced colleagues. This frequently leads to errors in diagnosis and selection of treatment. There are several ways in which computer based systems can assist in addressing such difficulties. One is to automate the process of data validation. At present it is up to the clinician to ascertain whether a measurement accurately reflects a patient's status, or is in error. While in many situations, signal error is clear from the clinical context, it can also manifest itself as subtle changes in the shape of a waveform. Without quite specialized expertise, clinicians may misinterpret measured data as being clinically significant, when it in fact reflects an error in the measurement system. The interpretations produced by a computer can be much more complex than an assessment of signal validity. It is also possible to design systems capable of diagnosing clinical conditions that can detect abnormalities in rare or complex cases based on the ECG waveform classification using trained backpropagation neural networks. Much of the research in medical artificial intelligence over the last two decades has been devoted to this area, and impressive diagnostic performances have been demonstrated in many specialized medical domains like monitoring ECG diagnosis in ICU [7]. Levels of interpretation Signal interpretation can vary from a low level assessment of validity to a complex assessment of clinical significance. A signal is first examined for evidence of artefact and the validated signal is then presented to the next layer in the interpretive hierarchy. Where a single channel signal contains sufficient information for a diagnosis, this is made. A flat portion of ECG trace is not diagnosed as an `asystole' because examination of the corresponding arterial waveform reveals pulsatile behaviour consistent with normal cardiac function. A higher level of interpretation is also possible, taking into account relevant contextual patient information where this is available. This level is concerned with making decisions based upon signal interpretations, and may include recommendations for further investigations or therapeutic actions. The tasks of artefact detection, single and cross-channel interpretation and decision support is examined in more detail. Methods of Interpretation Intelligent signal interpretation can be divided into two tasks. Firstly, distinct events within a signal are identified using pattern recognition methods e.g. detecting individual peaks in an ECG signal. Secondly a meaningful label is assigned to the detected events using pattern interpretation methods e.g. picking a QRS complex from a T

4 wave, and interpreting its clinical significance. There have been significant advances with techniques for performing both these tasks, and new methodologies have emerged, several specifically from research in AI. One of the more significant methods is pattern recognition. Working with Neural Networks The weights are obtained by a period of training, in which a net is presented with examples of the signal patterns it is intended to recognize, and the weights in the net are slowly adjusted until it achieves the desired output. A neural network thus encodes within its weights a discriminating function that is optimized to distinguish the different classes present within its training set. In theory, a network can approximate any such discriminant function. [5]. Despite initial claims of uniqueness for the computational properties of neural nets, it is becoming clear that they have clear and important relationships with a number of more traditional discrimination methods including Markov models, Bayesian networks, and decision trees. The properties of neural networks make them useful both for pattern recognition, and signal interpretation. The net not only recognizes a pattern, but is able to associate it with a predetermined diagnostic class. While the interpretive facility of nets has found numerous applications, it is limited by its inability to explain its conclusions. The reasoning by which a net selects a class is hidden within the distributed weights, and is unintelligible as an explanation. Nets are thus limited to interpreting patterns where no explanation or justification for selecting a conclusion is necessary. Since the need to justify a clinical diagnosis is recognized as an important part of the process of decision support, this limits the application of nets in such tasks. However neural networks can approximate the given ECG wave in supervised training (Fig 4 and Fig 5) and then further decision making an be accomplished using pattern recognition algorithms.[7] Rule ASY1: If heart rate = 0 then conclude asystole Rule ASY2: If asystole and (ABP is pulsatile and in the normal range) then retract asystole Fig 5: Approximated ECG by the Neural Network. In the presence of a zero heart rate, the expert system would first match rule ASY1 and conclude that asystole was present. However, if it next succeeded in matching all the conditions in rule ASY2 - that it had previously detected an asystole but could also detect a normal arterial waveform, then it would fire this second rule, which would effectively filter out the previous asystole alarm. If rule ASY2 could not be fired because the arterial pressure was abnormal, then the initial conclusion that asystole was present would remain. Block diagram and Description The patient interface is through electrodes; placed at the right arm, right leg, left arm and left leg. The block diagram of the system is shown in Fig 6. Fig.6. Block diagram of the system Fig 4: Original ECG wave given to the Neural Network. Examples of rules, which might be used to detect asystole and filter out false asystole alarms in the presence of a normal arterial waveform, might be: The instrumentation Amplifier: This amplifier is usually kept as the first stage of a good measuring instrument because of its high input impedance and high noise rejection ratio. In this circuit it is providing the main interface with the patient and is providing the initial gain to the received signal(s). Low Pass Filter: The low pass filter is a Butterworth filter with the center frequency set at 100Hz because ECG has the bandwidth of 0-

5 100Hz. This filter is also providing a gain of 10 to the signal(s). Notch filter: Notch filter is a specially designed filter, also called Hum filter, that is optimized for removal of the 50Hz AC hum that is mixed with the signal due to electromagnetic interference and/or power supply noise. This filter is necessary because the 50Hz hum is able to pass the low pass section described above as it falls in the required frequency range of 0-100Hz. Final Amplifier and Buffer section: This section has a variable gain and provides gain to the ECG according to the wish of the operator. It also has a buffer section with unity gain for further improving the strength of the signal. QRS filter: Power spectrum of a normal ECG signal has the greatest signal-to-noise ratio at about 17Hz, that is the frequency of the QRS complex in the ECG wave. As QRS complex is one of the main segments of the ECG, it is required to separate it from the whole signal for further analysis. A band pass filter with center frequency of 17Hz and BW of 6Hz is utilized for this purpose. Precision Rectifier: This section is used to remove the negative portion of the QRS complex before it goes in the sample and hold circuit. Threshold circuit and comparator: This section contains the pulse comparator that produces square wave with each QRS complex and the hold circuit for storing the threshold voltage value. It is used to store the threshold peak from the peak of the previous QRS complex. Monostable Multi-vibrator: This section is used to change the variable on time duty cycle of the pulse produced by the comparator to a constant on time duty cycle pulse. This is necessary for correct frequency matching of the signals (if a phase locked loop is used). Conclusion It is already the case that the clinicians, who use patient monitors, poorly understand much monitoring technology, and that clinicians are often unaware of how to use or interpret their output correctly. A Patient Monitoring System with ECG recording capability, which has incorporated neural networks, can be used for early diagnosis of abnormalities. The developed patient monitoring system has been tested with in the university on 15 volunteer students and the clinical abnormalities were presented to the machine using computer based simulation techniques. The neural network has shown excellent pattern approximation and classification abilities in supervised mode. The high level at which our system performs, will assist both in diagnosis and therapy, which means that it has a direct impact on patient care. It will often only be the clinician who will be in a position to assess the conclusions of such patient monitoring systems. [1] S K HASNAIN, Neural Networks and its application in biomedical engineering III rd Annual Hamdard Symposium of Hamdard College of Medicine & Dentistry & Hamdard University Hospital, Karachi, Jan [2] LAURENE FAUSETT, Fundamentals of Neural Networks, Architectures, Algorithms and Applications, Prentice Hall International 1994 [3] DONNA L HUDSON & MAURICE E COHEN, Neural Network and Artificial Intelligence for Biomedical Engineering, 1999, IEEE Press. [4] MERVIN J GOLDMAN, MD, Principles of Clinical Electrocardiography, LANGE Medical Publications California, 1982 [5] MICHAEL A ARBIB, The Handbook of Brain Theory & Neural Networks [6] JACEK M. ZURADA. Introduction to Artificial Neural Systems.PWS Publishing Company, 1995 [7] MUHAMMAD ASIM, Computerized Patient Monitoring System for ICU patient care, Final year project, Sir Syed University of Engineering and Technology, Karachi, Pakistan. Jan Syed Khursheed ul Hasnain is a faculty member of National University of Sciences & Technology (NUST) at Pakistan Navy Engineering College, PNS Jauhar, Karachi. His main area of interest is Control Systems and Neural Networks. He has registered himself with NUST for a Ph D degree in Neural Networks in Biomedical Engineering. 2. Mr. Muhammad Asim is a graduate of Biomedical Engineering Department from Sir Syed University of Engineering and Technology. His main areas of interest are Neural Networks and development of intelligent Medical Instrumentation and implantable devices.