Coimbatore , India. 2 Professor, Department of Information Technology, PSG College of Technology, Coimbatore , India.

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1 Research Paper OPTIMAL SELECTION OF FEATURE EXTRACTION METHOD FOR PNN BASED AUTOMATIC CARDIAC ARRHYTHMIA CLASSIFICATION Rekha.R 1,* and Vidhyapriya.R 2 Address for Correspondence 1 Assistant Professor, Department of Information Technology, PSG College of Technology, Coimbatore , India. 2 Professor, Department of Information Technology, PSG College of Technology, Coimbatore , India. ABSTRACT: Identification of cardiac arrhythmia class manually by viewing the Electro Cardio Graph (ECG) data will be much time consuming and may lead to false decision when the number of heart beats is large. Hence automation in arrhythmia classification evolved. The objective of this research work is to automatically classify cardiac arrhythmias with best classification accuracy through proper selection of feature extraction method. As per the recommendation of Association for the Advancement of Medical Instrumentation (AAMI), entire MIT-BIH arrhythmia database that consists of 44 recordings and around one hundred thousand heart beats is used for performance evaluation. Moreover, as per AAMI standard, heart beats are classified into five classes. Experiments are carried out by pre processing the ECG signals that includes filtering, followed by temporal and morphological feature extraction through Fourier Transform, Wavelet Transform (with Haar, Daubechies and Discrete Meyer families) and Stockwell Transform. Comparative analysis shows that combination of temporal and Daubechies wavelet (decomposition level 4) transform features along with dimensionality reduction through principal component analysis gives an average accuracy of 98.5%, sensitivity of 78.32%, specificity of 98.9%, positive predictivity of 85.93% and false positive rate of 1.101% when applied to Probabilistic Neural Network (PNN) Classifier at a spread factor of KEYWORDS: ECG beats, Morphological features, Arrhythmia classification. INTRODUCTION Cardiac Arrhythmias are disorders in the rhythm of heart. Patients with such abnormalities usually feel symptoms that include weakness, shortness of breath, dizziness and chest pain. However, many patients with arrhythmias do not feel any symptoms and the condition may be first discovered using standard method like Electrocardiography [1]. Electrocardiogram (ECG) is a diagnostic tool to assess the electrical and muscular functions of heart. Three major waves of electrical signals appear on the ECG. The first wave called P wave records the electrical activity of the atria. The second and the largest wave is the QRS wave and it records the electrical activity of the ventricles. The third wave is the T wave, and it records the heart s return to the resting state. Medical professionals study the shape and size of the waves, time interval between the waves and the rate and regularity of heart beats. This helps them to identify the irregular heart beat or cardiac arrhythmia. Statistics from the Centers for Disease Control and Prevention (CDC) have estimated sudden cardiac death rates at more than Table I: AAMI Heartbeat Classes 600,000 per year [2].Hence for faster and accurate diagnosis, automation in arrhythmia classification is necessary. AAMI has developed a standard for testing and reporting performance results of algorithms that aims at arrhythmia classification (ANSI/AAMI EC57:1998/(R) 2008). It recommends that, Heart beats can be classified to five classes namely class N (beats originating in the sinus node), class S (Supra ventricular ectopic beats), class V (Ventricular ectopic beats), class F (Fusion beats) and class Q (unknown beats including paced beats) as shown in table I. Entire dataset of MIT_BIH Arrhythmia database excluding 4 records that contain paced beats can be used for experimentation. Records has to be divided into two sets: one for training and another for testing, such that heart beat from one record (patient) is not used simultaneously for training and testing. Next section discusses state of the art techniques for arrhythmia classification. RELATED WORKS Several research works are been carried out for automation of Cardiac Arrhythmia classification system. Common procedure followed for automation involves (i) Pre-processing, (ii) Feature Extraction and (iii) Feature Classification.

2 Pre processing of recorded ECG signals are done in order to eliminate the important noises that degrade the classifier performance such as baseline wandering, motion artifact, power line interference and high frequency noise. Morphological filtering, Integral coefficient band stop filtering, Finite Impulse Response filtering,5-20 Hz band pass filtering, median filtering and wavelet based denoising using Daubechies wavelet are currently used by researchers for pre processing. ECG recordings obtained simultaneously from two different leads were segmented, temporal and morphological features extracted and classified using linear discriminant classifier model by [3]. Final decision was obtained by combining the classifier outputs of two leads and yielded a sensitivity of 75.9%, positive predictivity of 38.5% and false positive rate of 4.7% for class S. For class V, the same technique yielded a sensitivity of 77.7%, positive predictivity of 81.9% and false positive rate of 1.2%. Features extracted based on wavelet transform was given to PNN classifier with spread value of 0.03 and achieved more than 99% of average sensitivity, specificity and positive predictive value by [4]. Entire dataset of MIT_BIH database was considered by them. But the 44 records are not divided into two distinctive sets for training and testing. Statistical values of wavelet and S transform features in combination with temporal features were used by [5] to classify the recommended 5 classes and achieved average sensitivity of 69.38%. The same author reported his work in [6] by fusing statistical values taken from S transform and temporal features. But, in both the work, the training dataset includes 5 minutes of data from every patient record. Remaining 25 minutes of data were used for testing. This may lead to biased result. Wavelet based feature extraction in association with multilayered perceptron were used by [7] and obtained 100% accuracy. But this work classifies the record only into two classes: normal or abnormal.wavelet based features were reduced using Linear Discriminant Analysis (LDA) and classified with Support Vector Machine (SVM) classifier by [8]and achieved more than 99% accuracy for classifying six types of beats(not as per AAMI).Cost sensitive classification system that uses R-R interval, morphological descriptors and FFT based frequency features along with SVM classifier was developed by [9] and was able to achieve average accuracy of 97.2%.However, the above systems were tested with only 10 records selected from the MIT_BIH arrhythmia database.polyphase representation of wavelet filter bank along with PSO_SVM framework was used by [10] to classify six beats tested with recordings of 20 patients. Four types of ECG beats (Not as per AAMI) were classified using six different classifiers namely Multilayer Perceptron Neural Network (MLPNN), Combined Neural Network (CNN), Mixture of Experts (ME), Modified Mixture of Experts (MME), PNN and SVM. Comparisons of classifiers performance were analyzed by [11] using composite and diverse features extracted using Eigen vector methods and demonstrated that SVM trained on composite features and MME trained on diverse features achieved accuracy rates of 98.33%. Reimplementation of six classification methods that do not follow AAMI recommendation was done by [12].His results shows that the same classifier performs poor when AAMI standard is followed. Hence it was concluded in his research work that unbiased dataset as recommended by AAMI standards should be used for arrhythmia classification methods in order to obtain reliable results. MATERIALS AND METHODS In this work, MIT_BIH arrhythmia database [13] is used. It contains 48 half-hour excerpts of twochannel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory. The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mv range. Reference annotations for each beat were included with the database. As recommended by AAMI; full database is divided into two: Dataset 1(DS1) and Dataset 2(DS2) each was containing 22 records and is shown in table II. DS1 is used for training and DS2 for testing. Four records containing paced beats (102,104,107 and 217) were removed from analysis as specified by AAMI. Table II: Number of Heartbeats in each class Heartbeat N S V F Q type/dataset Full database DS DS Records considered for DS1:101,106,108,109,112,114,115,116,118,119,1 22,124,201,203,205,207,208,209,215,220,223 and 230. Records considered for DS2:100,103,105,111,113,117,121,123,200,202,2 10,212,213,214,219,221,222,228,231,232,233 and 234. Figure 1 show the methodology followed in the proposed work. Entire methodology can be broadly classified as, (1) Preprocessing, (2) Feature extraction and (3) Classification. Fig 1. Proposed System Preprocessing Noise Removal Wavelet based denoising procedure is used in which the raw ECG signals sampled at 360 Hz are decomposed up to level 9 using Daubechies ( db8 )

3 wavelet basis function [14]. Soft thresholding is applied to the detail coefficient at each level. Denoised ECG signal is computed based on the original approximation co efficient at 9 th level and modified detail co efficient of levels from 1 to 9. ECG segmentation MIT_BIH arrhythmia database contains continuous ECG recordings for 30 minutes. Continuous signal is segmented into individual ECG beats by first locating the R peaks. R peaks are detected using Pan Tompkins algorithm [15].According to that algorithm; the ECG signal is first passed over integer coefficient band pass filter with pass band 5-15 Hz to reduce the influence of muscle noise, 60 Hz interference and baseline wander. After filtering, the signal is differentiated, squared and passed over moving window integrator. Adaptive thresholds are used by the algorithm to locate the QRS complexes.99 samples before the QRS peak and 100 samples after the peak is considered as one ECG segment since it constitutes one cardiac cycle with P,QRS and T waves. Feature Extraction Segmented ECG recordings are made available for feature extraction. Two types of features are extracted from one ECG cardiac cycle: (a) Temporal features. (b) Morphological features. Temporal Features Three temporal features are extracted directly from RR-intervals of the preprocessed ECG signal. RRintervals are calculated as the interval between successive heartbeats. The following are the ways to extract temporal features: Pre-RR-interval: RR-interval between a given heartbeat and the previous heartbeat; Post-RR-interval: RR-interval between a given heartbeat and the following heartbeat; Average RR-intervals: mean of eleven RRintervals that include the given heartbeat at the center. Morphological Features In this work, Fourier transform, Wavelet transform and S-transform based morphological features are used to classify the ECG beats. (1) Fourier Transform Fourier Transform decomposes a signal into complex exponential functions and expresses it in terms of different frequencies of the waves that make up that signal. When frequency content of the ECG segment is analyzed, any deviations in the regular shape of normal ECG beat can be easily visualized. The Fast Fourier transform (FFT) is used in this work. FFT is a discrete Fourier transform (DFT) algorithm which reduces the number of computations needed for N points from O (N 2 ) to O (N log N) operations. The DFT is defined by the formula as given below, X = x e π k = 0,.., N 1 Where x n is the input sequence and X k is the transformed sequence of complex numbers. The advantage of Fourier Transform compared to the successive wavelet transform is that absolute phase is retained by this transform which provides more information than relative phase. But the drawback is that, time information such as when a particular frequency occurs is not available in the Fourier transformed signal which is particularly required in non-stationary signals like ECG. Wavelet Transform Wavelet transform provides time-frequency representation. The analysis is done by multiplying the ECG segment with the wavelet function and the transform is computed separately for different segments of the time domain signal. Discrete Wavelet transform (DWT) has been chosen in this work since the computation time required is less compared to Continuous Wavelet Transform.DWT analyzes the segment at different frequency bands with different resolutions by decomposing the segment into coarse approximation and detail information. The decomposition of the original segment is obtained by successive high pass and low pass filtering of that segment. This can mathematically be represented as follows, y [k] = x[n]g[2k n] y [k] = x[n]h[2k n] where, x(n) is the original ECG segment, g(n) and h(n) are the high pass and low pass filters respectively and y [k],y [k] are the outputs of high pass and low pass filters after sub sampling by 2.This procedure can be repeated for further decomposition and is shown in figure 2. The wavelet coefficients of discrete set of child wavelets are computed for a given mother wavelet ψ(t).the mother wavelet is shifted and scaled by powers of two in DWT and is given by the following equation,, ( ) = 1 t k2 2 2 where j is the scale parameter and k is the shift parameter. In this work Haar, Daubechies, Discrete Meyer wavelets are used and results are compared. Fig 2. Two level filter bank Stockwell Transform(S Transform) This transform has originated from short term Fourier Transform and wavelet transform. It provides multi resolution analysis and also retains the absolute phase of each frequency. It provides finer time-frequency localization property computing both amplitude and phase spectrum of discrete data samples. This is achieved by frequency dependent window for

4 analysis. The Discrete time S transform is expressed as below, S (j, n) = H[m + n]g(m, n) e π Where G(m,n) = e π α / and p, m and n=0,1..n-1.h[n] is the DFT of the given time series and α is the parameter that sets the width of the window for a given frequency. The output from the S transform is a complex matrix. The rows of this matrix correspond to frequency and column to time. But the drawback is that it requires higher complexity computation. Complete details of the feature set extracted and its corresponding actual dimension before dimensionality reduction is given in table III. Table III: Feature Set Extracted Dimensionality Reduction Principal Component Analysis (PCA) is used for dimensionality reduction. It helps the classification process to be carried out at faster rate and usage of less memory space. PCA converts correlated actual features into linearly uncorrelated features called principal components. Principal components exhibit decreasing variance and are uncorrelated with all other principal components. Most of the information will be conveyed with first few principal components itself. These reduced components can be used for subsequent classification. Dataset consisting of D dimensional samples were taken as input matrix. Mean vector, covariance matrix, Eigen vector and corresponding Eigen values were computed. Eigenvectors were sorted by decreasing Eigen values. Projection of data into new space is done by the inner product of input data matrix and sorted Eigen vector matrix. The first three principal components after data projection were taken for classification in this work. Classification Probabilistic neural network (PNN) is used for classification of ECG beats. It is a feed forward network with input, hidden, summation and output layer. When an input is given, the hidden layer computes the distances form the inputs and training input vectors to produce a vector whose elements indicate how close the input is to a training input. Summation layer sums these contributions for each class of inputs to produce as its net output a vector of probabilities. Output layer picks the maximum of these probabilities, and produces a 1 for that class and a 0 for the other classes.radial basis function (RBF) is used as the transfer function. RESULTS AND DISCUSSION The entire experiments were carried out using MATLAB 2014a.The performance of this work is evaluated based on the following parameters: Accuracy = (TP+TN) / (TP+TN+FP+FN), Sensitivity = TP / (TP+ FN), Specificity = TN / (TP+FP), Positive Predictive Rate = TP / (TP+FP), False Positive Rate = FP / (FP+TN),where, TP=True Positive, TN=True Negative, FP=False Positive, FN=False Negative and is shown in table IV.Several values of σ are taken for experimentation and the σ that gives best results is selected based on trial and error method. On performing experiments with various mother wavelets in Wavelet transform, it was found that Daubechies wavelet at a decomposition level of 4 provided better result at σ = compared to other mother wavelets.fs2 extracted from Time Frequency contour, Time-max amplitude Contour and Absolute values of complex S matrix of S transform outputs higher sensitivity and positive predictive rate compared to FS1 at a spread factor σ = Even though the other parameters are good for FS1 than FS2,the difference is very less. Hence for experimentation with fusion of transforms, features considered are temporal and FS2 of S transform. Daubechies wavelet with temporal features gives better results of 98.5% accuracy, 78.32% sensitivity, 98.9% specificity, 85.93% of positive predictive rate and 1.101% of false positive rate on an average compared to other methods. Even though S transform with FS2 provides highest PPR of 87.95%, the output obtained for other parameters is less compared to the fusion of transforms. When the same experiment was repeated with reduced number of training samples the performance was less compared to experiments with entire samples of database. This shows that performance of system directly depends on number of training samples. CONCLUSION In this work, we have classified the ECG beats taken from MIT-BIH Arrhythmia database into five classes as per AAMI standard. Experimental results shows that feature extraction based on fusion of temporal and Daubechies-4 of Wavelet transform in combination with PNN with spread factor σ = provides better results compared to other feature extraction methods. This system can be used in automated systems to help clinicians to be alerted in life threatening situation of patients. Future work is to further improve the sensitivity of the system and also to experiment with real time ECG signal.

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