Heart Function Monitoring, Prediction and Prevention of Heart Attacks: Using Artificial Neural Networks

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1 1 Heart Function Monitoring, Prediction and Prevention of Heart Attacks: Using Artificial Neural Networks D. K. Ravish 1 Associate Professor Dr. Ambedkar Institute of Technology, ravish_ait@yahoo.co.in Dr.K.J.Shanthi 2 Professor and Head Dr. Ambedkar Institute of Technology, shanthi.kj@ieee.org Abstract Heart Attacks are the major cause of death in the world today, particularly in India. The need to predict this is a major necessity for improving the country s healthcare sector. Accurate and precise prediction of the heart disease mainly depends on Electrocardiogram (ECG) data and clinical data. These data s must be fed to a non linear disease prediction model. This non linear heart function monitoring module must be able to detect arrhythmias such as tachycardia, bradycardia, myocardial infarction, atrial, ventricular fibrillation, atrial ventricular flutters and PVC s. In this paper we have developed an efficient method to acquire the clinical and ECG data, so as to train the Artificial Neural Network to accurately diagnose the heart and predict abnormalities if any. The overall process can be categorized into three steps. Firstly, we acquire the ECG of the patient by standard 3 lead pre jelled electrodes. The acquired ECG is then processed, amplified and filtered to remove any noise captured during the acquisition stage. This analog data is now converted into digital format by A/D converter, mainly because of its uncertainty. Secondly we acquire 4-5 relevant clinical data s like mean arterial pressure (MAP), fasting blood sugar (FBS), heart rate (HR), cholesterol (CH), and age/gender. Finally we use these two data s i.e. ECG and clinical data to train the neural network for classifying the heart disease and to predict abnormalities in the heart or it s functioning. Keywords-electrocardiogram; fibrillation; heart rate;heart attack;artificial neural networks I. INTRODUCTION Heart is the most important component of the cardiovascular system. It weighs around grams i.e. about the size of a fist. It beats around 2.5 billion times during as lifespan of years. The heart is electrically Nayana R Shenoy 3 Assistant Professor Dr. Ambedkar Institute of Technology, nayana.rshenoy@gmail.com S.Nisargh 4 Dr. Ambedkar Institute of Technology nisargh_dx@yahoo.com stimulated by a special unit called Sino atrial node. This region produces a definite potential and slowly discharges, thus sending an electrical impulse across the atria. This electrical impulse is very sequential in nature and responsible for the systole and diastole in the four chambers respectively. Disturbance in the electrical activity of the heart leads to a condition called Arrhythmia. Arrhythmia is a diseased condition of the heart in which there is a great variation of the heart s rhythm from the normal sinus rhythm. It may be fast (i.e. tachycardia) or slow (i.e. bradycardia) in nature. Some of common types of arrhythmias are a) Tachycardia, b) Bradycardia, c) Skipped beat or pause, d) Atrial, ventricular fibrillation, e) Pre mature ventricular contraction (PVC) and f) Rhythm and electrical conduction disorders etc. For precise and accurate treatment of arrhythmias we require combination of two data s i.e. ECG data and clinical data. The procedure involved in the data acquisition, processing, analysis and transmission of these data s is very expensive. This cost factor severely affects the quality and standards of medical care. Annual health reports by W.H.O shows that around 17.3 million people died from heart attacks in 2008 worldwide. Cardio vascular heart diseases are also leading cause of death in India, accounting for an estimated 2.25 million deaths in 2010 alone. This shows that Heart attacks are the number one cause of death globally and more people die from heart attacks compared to any other disease. Statistical and adaptive learning models are the two main approaches that have been applied to predict heart diseases based on ECG and clinical databases. Data mining and statistical analysis is a very powerful tool for distinguishing a diseased person from a healthy person. They utilize the hidden medical information in the vast patient databases and classify the heart disease into various types and categories. Thus this forms the foundation for any therapy or treatment /14/$31.00 c 2014 IEEE 1

2 2 to be given. We need an efficient system or a model that can predict heart attacks as the inputs are highly uncertain, non linear and continuously varying irrespective of time. Also each input is unique and different from the other inputs. This accounts for the need of highly non linear system that is flexible and adaptive in nature. Thus we use Artificial Neural Network (ANN) for the prediction and declaration of the results based on the inputs fed to it. Some of the features of artificial neural networks are: a) Non linearity b) Inputoutput mapping c) Adaptivity d) Fault tolerance e) Uniformity of analysis and design f) Neurobiological analogy g) Contextual information. Some of the other features are it is highly precise, very accurate and has a good learning rate. Due to these superior features we have chosen artificial neural network as the best tool for predicting and diagnosing the heart diseases. In this paper we have described five crucial steps taken in predicting heart diseases. They are Data acquisition stage, Processing stage, Network training stage, Disease predicting stage and finally Data transmission stage. II. LITTERATURE REVIEW The literature in this topic suggests the need for ECG classification and various approaches to perform the classification. Rosaria et.al [1] proposed that, the availability of low cost high performance computing technology encourages improvement in ECG by offering a reliable and comprehensive solution to the automatic diagnosis of the ECG. Anton Bartolo et.al [3] implemented the signal preprocessing using three-point FIR notch filter, running median filter. Eduardo et.al [5] proposed a neural network for P wave feature extraction using two asymmetric basis functions. Omer et.al [8] proposed a feed forward multilayer perceptron neural network with a single hidden layer for classification. Indu Saini et.al [13] classified subjects based on their RR intervals, systolic and diastolic blood pressure measured at different postures. The author has proposed K-Nearest Neighbor algorithm as a classifier for classifying the subjects based on lying and standing postures. R.Chitra et.al [17] adopted supervised learning algorithm for heart disease prediction at the early stages using patients medical records. The results where compared with known supervised classifier support vector machine. The patient information is classified using a cascaded neural network. Feng Xiao et.al [11] used evolutionary neural network as the predictor. The predicted HR can trace the actual HR. Feed forward neural network is trained with back propagation method. III. METHODS A. Feature selection In this paper, we have used the ECG data of the diseased patients from the Physio net ECG database. We have a total of 48 data s of which all of them are abnormal. The ECG features to be recorded are 1) QRS duration 2) R-R interval 3) P-R interval 4) Q-T interval 5) R-wave amplitude 6) P- wave duration 7) T wave duration. The clinical features to be recorded are 1) Mean arterial pressure (in ) 2) Fasting blood sugar (in ) 3) Heart rate (in BPM) 4) Cholesterol levels (in ) 5) Age factor (in years) 6) Smoking/Drinking/Tobacco factors 7) Diabetes factor. B. Detailed feature description The below table shows ECG features and their complete descriptions. TABLE I ECG EXTRACTED FEATURES AND THEIR COMPLETE DESCRIPTION. ECG features Feature description Measurements 1)QRS duration Indicates the atrial 0.08 seconds systole, atrial diastole and ventricular excitation respectively 2)R-R interval Indicates the heart rate in beats per minute 1 seconds 3)P-R interval 4)Q-T interval 5)Isoelectric line 6)R-wave amplitude 7)P-wave duration 8)T-wave duration Indicates the electrical signal generated by the sinus node is normal and travelling in a normal fashion in the heart. Indicates the flow of electrical impulse and blood from the atrial chambers to ventricles Indicates the resting time taken by the heart in a single beat Indicates the atrial diastole Indicates the rate of atrial excitation Indicates systole ventricular 0.16 seconds 0.36 seconds 0.12 seconds 1 milli volts 0.08 seconds 0.16 seconds In the Table I, we have selected eight features for the model to predict heart diseases. They are QRS duration (must be within 0.06 to 0.10 sec), R-R interval (must be within BPM), P-R interval (Indicates the proper functioning of Sino-atrial node), Q-T interval (Indicates the rate, velocity of blood flow from atrial to ventricular chambers), Isoelectric line (indicates the resting time in seconds taken by the heart in a single heart beat), R wave amplitude (indicates the rate of blood flow from atrial to ventricular chambers), P wave amplitude (Indicates the extent of atrial excitation), T wave amplitude (indicates the extent of ventricular relaxation). The table as shown below describes seven important clinical data s used to predict heart diseases International Conference on Contemporary Computing and Informatics (IC3I)

3 3 TABLE II CLINICAL DATA FEATURES AND THEIR COMPLETE DESCRIPTION. Clinical features 1)Mean Arterial pressure (M.A.P) Feature description Represents the mean arterial pressure 2) Fasting Blood Represents the Sugar ( F.B.S) sugar levels in blood during fasting 3)Cholesterol Represents the (CHOL) combination of good (HDL) and bad cholesterol (LDL) 4)AGE FACTOR Represents the class/age group of people highly prone to heart disease 5)DIABETES Indicates the blood sugar level (in ) 6)INTOXICANTS Indicates the intake of cigarettes, tobacco, alcohol etc Measurements (must be between ) (must be less than 120) (must be < 200) in years (Age>55 = Highly prone to heart diseases) Yes or no Frequently or not? Safe alcohol intake limit = 10-20ml per day out LDL cholesterol from the blood and keeping it away from building on the walls of arteries. The optimum range of HDL cholesterol is 60 or above. Thus the total cholesterol is the measure of LDL, HDL cholesterols and other lipid components. The optimum range of total cholesterol must be less than 200. Age factor is another important factor that must be considered in the prediction of heart disease. Arrhythmia s and CVD s are most likely to occur in a human being after the age of 55 years. This is mainly because of decreased physical exercise and ageing of the body. Only in some cases, due to haphazard and undisciplined lifestyle youngsters are also affected by heart diseases. Diabetes can be defined as a hereditary disease in which the blood sugar levels in a person are abnormally high. There are two types of diabetes: 1) Type-1diabetes 2) Type-2 diabetes. Type-1 diabetes is the case in which the body doesn t produce enough insulin to maintain the blood sugar levels.type-2 diabetes is the case in which insulin produced by the body is sufficient enough but not at all effective. Finally intoxicants are harmful substances that greatly increase the chances of a heart attack in a person. Alcohol, tobacco and cigarettes are most common example. Alcohol intake is good for the human body but in a limited quantity. Excess intake of alcohol may severely affect the health of an individual. The safe limits of alcohol intake are ml/day. Cigarettes and tobacco are very harmful for the human heart, because they can cause atherosclerosis, thus they must be completely avoided. C. Training of back Propagtion Neural Network In the above table we selected six statistical features, to be applied as inputs to the disease prediction model. In these four of the features are purely numeric and the other two are symbolic. Map is abbreviated as mean arterial pressure. It is given by the following formula: M.A.P = (D.P + S.P-D.P/3) (1) Where D.P = Diastolic pressure and S.P = Systolic pressure. The normal range of M.A.P is between 70 to 110. F.B.S abbreviated as fasting blood sugar, is the blood sugar test taken while the person is starving. It can be used to assess the risk of diabetes and its control. The normal range is between and anything out of this range indicates the risk of diabetes. CHOL indicates the cholesterol levels in the human body. It is basically a fatty substance which is basically found in all human cells. There may be two types of cholesterol: 1).LDL (bad cholesterol) 2).HDL (good cholesterol). LDL cholesterol, also called as bad cholesterol is harmful because it can cause atherosclerosis. It can also build up on the walls of the arteries and block the blood flow which increases the chances of heart disease. It must be less than 100.HDL cholesterol, also called as the good cholesterol is good for our body. It can protect us against the heart disease by taking Fig.1. Training states in a Back propagation network The training stage in a back propagation network is simulated in real time by using mat lab as an efficient tool. This diagram shown in Figure 1, describes the training process in a back propagated neural network. The training stage begins with feeding the network with desired no. of inputs. After the inputs are fed, these inputs are now sent to the computational or hidden layers as shown in the figure above. There are 100 hidden neuronal layers which are involved in the computational tasks. After each iteration in a computational process the error is said to be generated. Thus this error propagates back into the input layer from the hidden layers of the neuronal network. Thus the network seems to learn and minimize the errors after successive iteration. The output from the hidden layer is now sent to the output layer, where the final computation and display of the result occurs International Conference on Contemporary Computing and Informatics (IC3I) 3

4 4 IV. RESULTS AND DISCUSSION The results of the heart attack prediction model are categorized into two sections as shown below: 1) ECG data section: a). Input section: The below shown diagram Figure.2 shows the basic instrumentation amplifier which is used to acquire the ECG from a patient. The electrode used is 3 lead pre-jelled press button electrode. The IC s used are AD620A (1no.s), which acts as the basic signal acquirer and Op07 (5no.s), which acts as the signal amplifier. After the signal is acquired by the acquisition circuit the signal is passed onto the noise filtering circuit. The noise filtering circuit consists of a low pass filter and high pass filter as shown above. The filtered signal is sent to the notch filtering section for removing the 50 Hz noise which was acquired during data acquisition from the power source. The filtered and amplified ECG signal is now fed to the artificial neural network to predict abnormalities if any. We have acquired the ECG signal via PCB implementation as shown below in the Figure 3. b) Output section: The acquired ECG data via PCB board is interfaced into software environment like mat lab as shown below in Figure 4. Fig.4. Display of the acquired ECG data via mat lab user interface This ECG signal is now differentiated (1 st and 2 nd derivative) as shown below. These steps are taken in order to find the heart rate of a person. The signal is sampled at a rate of 1024 Hz and then differentiated as shown in the below Figure 5. Fig.2. Basic instrumentation amplifier (Data acquisition circuit) Fig.5. Differentiation techniques applied on ECG signal Fig.3. PCB implementation of instrumentation amplifier The R wave is observed as sharp spike in the Figure 6 below. The R-R intervals are measured by the use of successive ECG algorithms and heart rate is calculated. Finally based on the statistical data, the heart rate of a person is assigned target output as normal if and only if it lies in that specified range else it is declared as abnormal. This is as shown below in the outpu window International Conference on Contemporary Computing and Informatics (IC3I)

5 5 Fig.6. Heart rate calculation and disease prediction 2) Clinical data section: The standard clinical data chart which is considered as a pre requisite standard for determining the heart disease. The low, normal, high level of these clinical data s is as shown below. Clinical Data Low Normal High MAP FBS Fig.7 Training states in a Back propagation network with GA features HR Cholesterol Levels BPM BPM BPM In this paper we have chosen with all the features tested the ANN and also using genetic algorithm (GA) important clinical features are evaluated by its fitness function. The GA takes four important features to determine whether the person is healthy or not. The inputting of alll the four clinical data s is done in a row matrix format respectively. The output is displayed column wise matrix as shown in the figure. The output indicates three possibilities they are normal, abnormal and predicted. If the output is 000,001,010,100 in column format then we can conclude that the patient is normal. If the output is 110,011,101 then the patient is said to be abnormal. Finally if the output clinical data is 111 then the case is predicted. This means the patient s condition is highly abnormal and he is highly prone to heart attacks. These cases are displayed separately in different output windows as shown in the below figures. Fig.8.Clinical data s showing normal levels Fig.9. Clinical data s showing abnormal levels 2014 International Conference on Contemporary Computing and Informatics (IC3I) 5

6 6 Fig.10. Clinical data s showing prediction (High risk) levels In the context of automated model to assess the heart ailment, therapy and their interrelations with physiological parameters like HR to facilitate fusion of clinical data interpretation. Based on the obtained information, monitoring of parameters might be realized and even the results facilitate to take precautionary measures to control the MI. Furthermore in the conclusion and future work, this research at the outset is the starting preliminary work found is to be helpful in identifying risks in heart condition. In future work tread mill ECG test (TET) data, more clinical trials and cardiologist suggestions in clinical parameter help to develop a working model to predict myocardial infarction accurately is possible in near future. The work is encouraging and is in progress. ACKNOWLEDGMENT We are thankful to the Management PVP welfare trust, Dr.Ambedkar Institute of Technology, Bangalore, India. 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