Artificial Neural Network Based Automatic Cardiac ... - IEEE Xplore

Artificial Neural Network Based Automatic Cardiac Abnormalities Classification S. Issac Niwas, Department of ECE, Mepco Schlenk Engg. College, Sivakasi, Tamil Nadu, India. [email protected]

R. Shantha Selva Kumari, Assistant Professor, ECE Dept., Mepco Schlenk Engg. College, Sivakasi, Tamil Nadu, India. [email protected]

Dr.V.Sadasivam, Professor and Head, CSE Department, Manonmanium Sundaranar University, Tirunelveli,India.

Abstract Automatic Detection and classification of Cardiac Arrhythmias from a limited number of ECG signals is of considerable importance in critical care or operating room patient monitoring. We propose a method to accurately classify the heartbeat of ECG signals through the Artificial Neural Networks (ANN). Feature sets are based on Heartbeat intervals, RR intervals and Spectral entropy of the ECG signal. The ability of properly trained artificial neural networks to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the interpretation of ECG signals. In the present work the ECG data is taken from standard MIT-BIH Arrhythmia database. The proposed method is capable of distinguishing the normal beat and 9 different arrhythmias. The overall accuracy of classification of the proposed approach is 99.02%. The results of the analysis are found to be more accurate than the other existing methods. Detection and classification of cardiac signals is important for diagnosis of cardiac abnormalities and hence any automated processing of the ECG that assists this process would be of assistance and is the focus of this paper.

1. Introduction Computer based analytical tools for the in-depth study and classification of data over daylong intervals can be very useful in diagnostics. Classification of ECG signal is an important area in biomedical signal processing. Heart Arrhythmias result from any disturbance in the rate regularity and site of origin or conduction of the cardiac electric impulse [1]. Detection of these arrhythmias is well researched and successful detectors have been designed with high sensitivity and specificity [2]-[4].Many arrhythmias manifest as sequences of heartbeats with unusual timing or ECG morphology. An important step toward identifying an arrhythmia is the classification of heartbeats. The rhythm of the ECG signals can then be determined by knowing the classification of consecutive heartbeats in the signal. Several algorithms have been developed for automated classification of ECG beats. These techniques using a variety of features to represent the ECG and a number of classification methods. Features include template matching [5], ECG morphology [6], [7], heartbeat interval features [6]-[9], Higher order cumulant features [8], Karhunen-Loeve expansion of ECG morphology [9], Frequency-based features [10], Hidden Markov models [11], and Hermite polynomials [13]. Classifiers methods employed include linear discriminants [10], Back

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Propagation neural networks [6]-[8], Self-organizing maps with learning vector quantization [9], and self-organizing networks [12]. In the present work, three parameters extracted from the ECG signals are used for the classification of Electro cardiac signals into 10 classes. The parameters like Heartbeat intervals [18], RR intervals [18] and Spectral entropy [16] of the ECG signal are used to extract features from the non-stationary ECG signal. A supervised artificial neural network (ANN) is developed to recognize and classify the nonlinear morphologies. ANN trained with back propagation algorithm, classifies the applied input ECG beat to appropriate class.

2. Materials and Methods 2.1. ECG Data Different ECG Databases are used for the present work such as Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, European Society of cardiology database, Creighton University Ventricular Tachyarrhythmia database, MIT-BIH Atrial Fibrillation database, MIT-BIH Supraventricular Arrhythmia database ,MIT-BIH Malignant Ventricular Arrhythmia database and several ECG clinical databases, which includes recordings of many common and life-threatening arrhythmias. 2.2 ECG Filtering method The digitized ECG signals include baseline wandering; power line interference (see Figure1.a).The ECG filtering method utilizes a filtering unit to remove artifact signals from the ECG signal. ECG signals are filtered with two median filters to remove the baseline wander. Each signal is processed with a median filter of 72-ms width to remove QRS complexes and P-waves. The resulting signal is then processed with a median filter of 216 ms width to remove T-waves. The signal resulting from the second filter operation contained the baseline of the ECG signal, which is then subtracted from the original signal to produce the baseline corrected ECG signal.

3. Neural network classifier The decision making process of the Artificial neural networks ANN is holistic, based on the features of input patterns, and is suitable for classification of biomedical data. Typically, multilayer feed forward neural networks can be trained as non-linear classifiers using the generalized Back propagation algorithm (BPA). The BPA is a supervised learning algorithm, in which a sum square error function is defined, and the learning process aims to reduce the overall system error to a minimum. Classification of arrhythmias is a complicated problem. To solve this two hidden layers are taken in a feed-forward neural network. In this work all neurons uses sigmoid activation function. By using back-propagation algorithm the network has been trained with moderate values of learning rate and momentum. The weights are updated for every training vector, and the termination condition is that the sum square error reaches a minimum value. The connection weights are randomly assigned at the beginning and progressively modified to reduce the overall system error. The weight updating starts with the output layer and progresses backward. The weight update is in the direction of ‘negative descent’, to maximize the speed of error reduction [17]. For effective training, it is desirable that the training data set be uniformly spread throughout the class domains. The available data can be used iteratively, until the error function is reduced to a minimum. The input layer consisted of nodes, and, in the subsequent hidden layers, process neurons with


the standard sigmoid activation function is used. The output layer has five neurons, to divide the output domain into ten classes (0000 to 1010).

(a)

(c)

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(d)

Figure 1. (a) Input ECG signal-(Top) and Baseline corrected signal-(Bottom), (b) Heartbeat detection method, (c) Heartbeat interval detection method, (d) QRS signal segmentation

4. Disease classification using Neural Network For the purpose of this study, the cardiac disorders are classified into ten categories, namely, Normal Beat (NB) , Left Bundle Branch Block (LBBB),Right Bundle Branch Block (RBBB), Atrial Premature beat (AP), Supraventricular ectopic Premature beat (SP), Premature VentricularContraction (PVC), Atrial Fibrillation (AF),Ventricular Fibrillation (VF),Sick Sinus Syndrome (SSS), Fusion of ventricular and normal beat(FVN).

5. Feature Extraction Features relating to Fiducial point intervals; Heartbeat intervals and Spectral entropy are calculated for each heartbeat type. The heartbeat detection module [18] attempts to locate all heartbeats (see Figure1.b). 5.1 RR-Interval Features Heartbeat fiducial point intervals (henceforth called RR-intervals) are defined as the interval between successive heartbeat fiducial points. The parameter like average RR-interval is


extracted from the RR sequence (see Figure.1c) which is derived by the heart-beat detection algorithm [18]. The average RR-interval represents a combination of the averaged latest n intervals RRmean with the shortest one of them RRmin in a ratio of n: 1, i.e. (n RRmean + RRmin) / (n+1). 5.2 Heartbeat Interval Features The QRS-duration feature relating to heartbeat intervals are calculated after heartbeat segmentation [18]. The QRS duration (see Figure 1.d) is the time interval between the QRS onset and the QRS offset. 5.3 Spectral entropy There are a number of concepts and analytical techniques directed to quantifying the irregularity of stochastic signals, such as the ECG. One such concept is entropy. The concept of spectral entropy originates from a measure of information called Shannon entropy. The Power spectral density (PSD) can be obtained by using Fourier transformation (FT) technique. The PSD represents the distribution of power as a function of frequency. Normalization of the PSD with respect to the total spectral power yields the probability density function (PDF). Application of Shannon’s channel entropy gives an estimate of the spectral entropy of the process, where entropy is given by, § 1 · ¸ [1] H = ¦ p f log ¨ ¨ ¸ p f © f ¹ where p f is the PDF value at frequency f. Heuristically, the entropy is interpreted as a measure of uncertainty about the event at f. Thus entropy can be used as a measure of system complexity. The spectral entropy H (0 ≤ H ≤ 1) describes the complexity of the HRV (Heart rate variability) signal. This spectral entropy H is computed for the various types of cardiac signal.

6. Results and Discussion ECG records with normal beats and different types of arrhythmias are selected from the MIT-BIH arrhythmia database for the analysis. The Accuracy of an ECG classifier is defined as the ratio of the number of beats correctly classified to the total number of beats tested. The trained network has been tested in the retrieval mode, in which the testing vectors are not taking part in the training process. Table 1 list the result of ANN models used in the classification of ECG. The efficiency classification in the testing mode is 99.02%. The results have been compared with those reported in the literature. The comparison is made with different types of beat recognition systems, viz., Discrete Fourier Transform (DFT) [17], Mixture-Of-Experts (MOE) [9], Fuzzy Hybrid Neural Network (FHNN) [8], Fourier Transform Neural Network (FTNN) [2], and Discrete Wavelet Transform (DWT) [17].The results reported in the literature are based on a smaller set of arrhythmias. The proposed algorithms show the comparable performance even when all 10 different types of ECG signals have been included in the dataset (Table 2).

7. Conclusion The ECG signal can be used as a reliable indicator of heart diseases. In this paper, the neural network classifier is presented as the diagnostic tool to aid the physician in the analysis of heart diseases. The most important factor in determining whether an automatic ECG diagnosis


system is successful or not is the accuracy of event detection. The accuracy of the tools depends on several factors, such as the size and quality of the training set, the rigour of the training imparted and also the parameters chosen to represent the input. In this preliminary evaluation, only the signals from MIT/BIH database are used. Expansion will be made to include all 12 lead information in the future for better diagnosis accuracy and disease classification. It is believed that the chosen feature sets combined with the neural network provide a good solution for automatic ECG diagnosis system in the future. Table 1. Overall performance of the proposed method ECG Signal Type NB LBBB RBBB AP SP PVC AF VF SSS FVN Total

Number of Dataset used for Training 25 20 30 25 15 40 20 20 30 25 250

Number of Dataset used for Testing 20 15 25 15 10 25 15 15 20 20 180

Training Accuracy in % 100 100 100 100 100 100 100 100 100 100 100

Testing Accuracy in % 98.90 96.77 96.27 91.35 92.00 98.92 95.96 95.78 98.27 96.41 99.02

Table 2. Comparison of different ECG classifiers Method Proposed method MOE FHNN DWT FTNN DFT

No of Arrhythmia Types 10 4 7 13 3 10

Accuracy in % 99.02 94.00 96.06 97.00 98.00 89.40

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