Neural Network based Heart Arrhythmia Detection and Classification from ECG Signal

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1 Neural Network based Heart Arrhythmia Detection and Classification from ECG Signal 1 M. S. Aware, 2 V. V. Shete *Dept. of Electronics and Telecommunication, *MIT College Of Engineering, Pune 1 mrunal_swapnil@yahoo.com, 2 vvshete100@gmail.com Abstract- Now a day s Heart arrhythmia needs to be treated specially as it became a prime cause of death occurrence of people. Such number of death could be decrease by prediagnosis status of heart signals. This paper presents the new automated arrhythmias detection method. For identification of arrhythmia continuous wavelet transform (CWT) is used for feature extraction from ECG signal and the purpose of using CWT is to reduce training time of NN classifier without losing system accuracy. Keywords-ECG, CWT-Continuous Wavelet Transform, NN- Neural Network. I. INTRODUCTION According to World Health Organization (WHO) an estimated 17.3 million people died from heart disease in 2008 and this number of death will increase to reach 23.3 million by Hence heart diseases become very concerned disease. By adopting a new method for early detection of heart health, it becomes possible to reduce the number of death and prolong the length and quality of life. interval is the duration between two successive normal ECG beat, measures the heart rate of the person. Heart Rate = [60 RR interval] beats per min. (1) Normal heart rate is beats per min. The variation in heart rate from the normal rate indicates the abnormal behavior of heart which is symptom of heart arrhythmia. The two types of arrhythmia beats along with Normal beat are as shown in table1 [2] Sr. Type No. arrhythmia 01 Normal Beat Table1: Arrhythmia beats of 02 Left bundle branch block beat Representative ECG beat 03 Right bundle branch block beat Fig.1: Normal ECG Signal [1] The normal Electrocardiogram (ECG) signal is as shown in figure 1. ECG reflects the electrical activity of heart caused by heart contraction and reflection and has been widely used for analysis of heart behavior. ECG signal composed of four standard components known as P wave, QRS complex, and T wave followed by small U wave which sometimes invisible. Any abnormality with respect to such component indicates a heart arrhythmia. RR The aim of this paper is to develop computerized diagnosis method for detection of heart arrhythmia by examining the ECG signal. The ECG signal analysis is very simple, reproducible and inexpensive method. There are numerous methods are available for feature extraction, detection and classification of heart arrhythmia. Some of them summarizes as: discrete wavelet transform for feature extraction and intersecting spheres network classification by Dokur Z, Olmez T in 2001[3], cumulants of the second, third and fourth order for feature extraction along with fuzzy hybrid neural network classifier by Osowski S, Linh TH in 2001[4], fuzzy c-means clustering, principle component analysis and wavelet transform for feature extraction and artificial neural network for classification by Rahime Ceylan and Yuksel Ozbay, in 2007[5], discrete wavelet transform for feature extraction and multilayer perceptron neural network for classification by Froese, Hadjiloucas, 9

2 Galvao, Becerra & Jose Coelho in 2006[6], discrete wavelet transform for feature extraction and probabilistic Neural Network for classification by Yu & Chen in 2007[7], RR-interval method for feature extraction and self organizing map as a classifier by Lagerholm M, Peterson C, Braccini G, Ebendrandt L, Sornmo L, in 2000[8], RR-interval based feature extraction and knowledge based classifier by MG Tsipouras, DI Fotiadis, Sideris in 2002[9]. This paper is organised as follows: in section II the methodology is described. In section III experimental analysis and results are described. Finally in section IV conclusion is described. II. METHODOLOGY Block diagram of proposed algorithm is shown in fig.2. The method of arrhythmia detection is of three main stages that are pre-processing i.e. noise removal, feature extraction, and finally classification of arrhythmia. Raw ECG signal Noise Removal R peak detection Sample selection Feature Extraction Classification Using NN ratio as well as it preserve the original morphological feature of ECG signal. The removal of noise include two stages which are removal of baseline drift and removal other noises (motion artifact, muscles artifacts etc.). In this paper smoothing filter is used to remove baseline drift. The smoothing algorithm converts noisy data, which is the 1, X2, X3, X4 X n in to X k s X,... average of odd points of noisy data and it can be expressed by following equation: i n k s i X X 2n 1) n ki /( (2) The formula for removal of baseline drift from noisy ECG signal can be expressed as: Z Z1 Z2 (3) Where, Z is the signal free from baseline drift, Z1 is original signal, Z2 is obtained average value from original signal (Z1) using smoothing filter. After the removal of baseline drift the next step is to remove the other noises. Here Savitzky-Golay filter is used to get clean ECG signal. Savitzky-Golay filter is also known as a digital polynomial filter or a least-squares filter which is useful to preserve the important information such as peaks and valleys of the ECG signals. The Savitzy-Golay filter is define by following equation n Ai X ki in X k (4) s n A in i Where X k s is smoothed data and Ai are weighting coefficients to perform smoothing operation. B. R peak detection: Fig.2: Block schematic of proposed algorithm A. Removal of Noise: N L R The ECG signal is examined and verified by an expert and extract the important parameters which are needed to determine the health of heart. As ECG signal composed of P wave, QRS complex, T wave, and U wave but among of all these components of ECG signal most of the information lies around R peak. (10) The common type of noises or disturbances that can corrupt the ECG signal are baseline drift, power line drift, muscle artifact, motion artifacts etc. The presences of such noise can affect the diagnosis of heart disease (heart arrhythmia). Hence denoising of ECG signal became mandatory to improve the performance of proposed method. The different algorithm which are used to denoising the ECG signal increases the signal to noise After the detection of R peak the selected samples after R peak are used for feature extraction. In proposed method the accuracy of detection of heart arrhythmia depends upon accurate detection R peak. C. Sample Selection: In this paper the proposed method detects two types of arrhythmias that are left bundle branch block beats 10

3 (LBBB), right bundle branch block beats (RBBB) including Normal beats (N). To detect each arrhythmia beat 150 samples after R peak of ECG signal were selected. The most relevant information about the arrhythmia contains in these samples. These selected samples acts as input vector to the CWT for feature extraction. D. Feature Extraction: Early time domain method was used for analysis of ECG signal. But this method is not useful to provide all feature information of ECG signal. Hence later Fourier transform was used but it fails to provide time information. So the method which is able to provide time-frequency information called Short Term Fourier Transform (STFT) was used. But it suffers from the drawback of fixed window size, which makes it fail to provide all necessary information of ECG signal. E. Neural Network Classifier: Neural networks are very popular in classification, pattern recognition application. The extracted feature vector that is nothing but CWT coefficients of selected sample vector is submitted to NN classifier. The quality of output of NN classifier is based on the selected input vector patterns, because the failure in selection of proper input vector leads to failure of the best classifier also [12]. For identifying the cardiac beats a feed forward neural network with one input layer, one hidden layer and one output layer is used as shown in fig.4. The network is trained by back propagation gradient descent algorithm. The weights and biases are updated up to the mean squared error of 1e-4. Input layer is of 1500 neurons, hidden layer is of 6 neurons and finally output layer is having 3 neurons first for Normal beats (N), second for Left bundle branch block beats (LBBB), and third for Right bundle branch block beats (RBBB). Hence now a days in non-invasive electro cardiology wavelet transformation is a promising method for feature extraction [10]. Wavelet transform can be defined as advancement to Fourier transform because wavelet transform work on time-scale region instead of time-frequency region as shown in fig. 3. The wavelet transform is broadly classified as continuous wavelet transform (CWT) and discrete wavelet transform (DWT). In this paper to extract the feature from selected sample vector CWT is used. Mathematically the continuous wavelet transform (CWT) of signal x t with a family of wavelet function equation: x w a,b is defined by following 1 t b, (5) a a a b x( t) * dt Where t is called mother wavelet, a is scale factor which is inverse of frequency, a and b is temporal translation of the function. To detail and useful analysis of ECG signal, CWT scale range will be from x=5 through x=20 [11]. In this study the signal is analyzed with scale range x=6 through x=15. Fig.3: Wavelet analysis Input layer Hidden layer Output layer Fig.4: Generalize architecture of NN III. EXPERIMENTAL RESULT The MIT-BIH arrhythmia database is used for development and evaluation of proposed method using MATLAB software. To remove baseline drift which is generally a low frequency signal with a frequency range in between 0 to 0.5 Hz [13] smoothing filter is used. Fig. 5 shows the ECG signal with baseline noise marked in red color and fig. 6 shows the ECG signal after removing baseline drift. 11

4 To extract the feature of arrhythmia beat the 150 samples after R peak (as shown in fig.8) are selected and are submitted to CWT with Haar mother wavelet. Haar mother wavelet is more suited to extract the feature from ECG signal as well as for data classification. The extracted CWT features from record 101 are shown in table 2. Table2: Extracted feature vector of record 101 Fig. 5: ECG signal with baseline noise of record 101 Fig.6: ECG signal without baseline noise of record 101 After the removal of baseline drift the ECG signal is again filtered using Savitzky-Golay filter to remove all other noises from the ECG signal. Fig. 7 shows the filtered ECG signal free from all noises. The extracted feature vector is applied to trained neural network which then classify the ECG beats in one of the arrhythmia type i.e. N, LBBB, RBBB. Following table 3 shows the analysis of record 101. Table3: Percentage recognition of arrhythmia type Fig.7: Clean ECG Signal of record 101 Fig.8: ECG Signal of record 101 with R Peak 12

5 Table 4: Results of algorithm Norm al LBBB RBBB Overall Performance TP FP TN FN A(%) Se(%) Sp(%) Pp(%) The proposed algorithm is assessed by calculating Accuracy (A), Sensitivity (Se), Specificity (Sp) as shown in table 4. As per the table, the overall result of method has 95.69% A, 93.54% Se, 96.77% Sp, and 93.54%Pp. IV. CONCLUSION This study is on detection and classification of arrhythmia beats. The heart beats are different for different person and all these beats are having different variations with nonlinear nature. Thus the proposed computerized system will be helpful for early detection of heart status and to decrease the death percentage of human which occurs due to the heart disease. ACKNOWLEDGEMENT The authors thank Department of Electronics and Telecommunication, College of Engineering for technical support to this proposed research work. REFERENCES [1] Kabir M. D., Ashfanoor and Celia Shahnaz. International Journal of Research and Reviews in Applied Sciences (IJRRAS), Vol 1., Issue 3, Pp , Sakar N (2003). Elements of Digital Signal Processing. Khanna Publishers, India [2] Analysis and Interpretation of the Electrocardiogram. A Self-Directed Learning Module. Queen s University, Department of Emergency Medicine. [3] Dokur Z, Olmez T, ECG beat classification by a hybrid neural network. Comp Meth Prog Biomed 2001; 66: [4] Osowski S, Linh TH, ECG beat recognition using fuzzy hybrid neural network. IEEE Trans Biomed Eng 2001;48: [5] Ceylan,R., & Ozbay, Y.,Comparison of FCM,PCA and WT techniques for classification ECG arrhythmia using artificial neural network. Expert System with Application,2007; 33, [6] Froese, T., Hadjiloucas, S., Galvão, K. H. R., Becerra, V. M., & José Coelho, C..Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms. Pattern Recognition Letters, 2006; 27, [7] Yu, S., & Chen, Y., Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters,2007; 28, [8] Lagerholm M, Peterson C, Braccini G, Ebendrandt L, Sornmo L, Clustering ECG complexes using hermite functions and self- organizing maps. IEEE Trans Biomed Eng 2000;47: [9] Tsipouras MG, Fotiadis DI, Sideris D, Arrhythmia classification using the RR-interval duration signal. In: Murray A, editors. Computers in cardiology. Piscataway: IEEE, p [10] A.Gautam, M. Kaur, ECG analysis through continuos wavelet transform(cwt), IOSR Journal of engg., April 2012,Vol.2(4)pp: [11] P.Ghorbanian,A.Ghaffari,A.Jalali,C.Nataraj, Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier,ieee 2010 [12] I. Guler, E. Ubeyli, ECG beat classifier designed by combined neural network model, Pattern Recogn.38(2)(2005) [13] Y.C. Yeh, W.J. Wang, QRS complexes detection for ECG signal: the Di_erence Operation Method," Computer Methods and Programs in Biomedicine, vol. 91, no. 3, pp [14] 13