CHAPTER IV PREPROCESSING & FEATURE EXTRACTION IN ECG SIGNALS

CHAPTER IV PREPROCESSING & FEATURE EXTRACTION IN ECG SIGNALS are The proposed ECG classification approach consists of three phases. They Preprocessing Feature Extraction and Selection Classification The complete process of the proposed approach is shown in the figure 4.1. MIT-BIH Arrhythmia Database Noise Removal using Morphology Filter Feature Extraction and Selection using DWT and AR Modeling Classification using Machine Learning Techniques Figure 4.1: Block Diagram of the Complete Process of ECG Signal Classification 4.1. DATASET DESCRIPTION The experiment conducted on the basis of ECG data from the MIT BIH arrhythmia database [107]. This database was the first commonly available set of standard test material for evaluation of arrhythmia detectors and has been 64

exploited for that purpose in addition for basic research into cardiac dynamics at more than 500 sites worldwide. The MIT-BIH Arrhythmia Database (Figure 4.2) includes 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Figure 4.2: MIT BIH Arrhythmia Database 65

Twenty-three recordings were selected arbitrarily from a set of 4000 24- hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were chosen from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample. The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mv range. Sample ECG wave form is shown in figure 4.3. Figure 4.3: Sample ECG Wave from Physionet 2011 (MIT-BIH) In particular, the considered beats refer to the following classes: Normal sinus rhythm (N), Atrial premature beat (A), Ventricular premature beat (V), Right bundle branch block (RB), left bundle branch block (LB) and paced beat (/). The beats were selected from the recordings of 20 patients, which correspond to the following files: 100, 102, 104, 105, 106, 107, 118, 119, 200, 201, 202, 203, 205, 208, 209, 212, 213, 214, 215 and 217. Notes and statistics shown in figure 4.4 describe the content of the record 100. 66

Figure 4.4: Notes and Statistics of Record 100 in MIT BIH Arrhythmia Database The wave forms of different diseases are shown in the following figure. (a) Wave form of Normal Beat for Patient ID: 100 67

(b) Wave form of Atrial Fibrillation Disease for Patient ID: 201 (c) Wave form of Ventricular Premature Beat for Pat Patient ient ID: 106 (d) Wave form of Right Bundle Branch Block Disease for Patient ID: 118 (e) Wave form of Paced Heart Beat for Patient ID: 102 1 68

(f) Wave form of Left Bundle Branch Block for Patient ID: 214 Figure 4.5: Six Types of ECG Signal Wave forms 4.2. PREPROCESSING The performance of the classification not only based on the classifier, however it is also based on the features and enhanced ECG signal processing. Morphology Filter (MF), a built-in in function in MATLAB which is used to remove the noise component at the same time preserving the ECG morphology and time domain features. ECG signals taken from the MIT-BIH arrhythmia database is used. MF based pre-processing removes high frequency noise components and baseline drift, in addition to preserve ECG morphology. MF has the good quality of preserving the sharpness of the QRS complex. MF filters the baseline drift and high frequency ECG noise with low distortion as present in the original ECG signal and with less computational burden [118]. The following command is used for preprocessing ECG signal using Morphology Filter. Rsig=bwmorph(sig, clean ); 69

Figure 4.6: Preprocessed ECG Signal of Record 100 4.3. FEATURE EXTRACTION AND SELECTION ECG beat recognition and classification is performed with temporal and morphological features. Since these features are very at risk to variations of ECG morphology and the temporal characteristics of ECG, it is difficult to distinguish one from the other on the basis of the time waveform or frequency representation [66, 68]. In this phase two different classes of feature set are used belonging to the isolated ECG beats including; auto-regressive model parameters and the variance of discrete wavelet transform detail coefficients for the different scales (1 6 scales) [97]. A. Wavelet Transformation In this research work, the feature extraction was done by applying Discrete Wavelet Transform. The benefit of the wavelet transformation n lies in its capacity to highlight the significant amount of information about the ECG signal. 70

Physiological signals used for diagnosis are frequently characterized by a non-stationary time behavior. For such patterns, time and frequency representations are desirable. The frequency characteristics in addition to the temporal behavior can be described with respect to uncertainty principle. The wavelet transform can represent signals in different resolutions by dilating and compressing its basis functions [72]. While the dilated functions adapt to slow wave activity, the compressed functions captures fast activity and sharp spikes. The most favorable choice of types of wavelet functions for pre-processing is problem dependent. In this phase, Daubechies wavelet function (Db5) which is called compactly supported orthonormal wavelets [101]. By making discretization the scaling factor and position factor the DWT is obtained. For orthonormal wavelet transform, x(n) the discrete signal can be expanded in to the scaling function at j level, as follows: (4.1) where represents the detailed signal at j level. Note that j controls the dilation or contraction of the scale function and denotes the position of the wavelet function and represents the sample number of the. Here represents the set of integers. The frequency spectrum of the signal is classified into high frequency and low frequency for wavelet decomposition as the band increases. Wavelet transform is a two-dimensional timescale processing method for non-stationary signals with adequate scale values and shifting in time [102]. Multi resolution decomposition can efficiently provide simultaneous characteristics, in term of the representation of the signal at multiple resolutions corresponding to different time scales. Feature vectors are constructed by the normalized variances of detail coefficients and P-QRS-T coefficients of the DWT which belongs to the related scales. 71

commands. The wavelet decomposition of ECG signal was done by the following axes(handles.axes1); plot(sig); ylabel('signal'); axes(handles.axes8); [a1 d1]=dwt(sig,'db5'); plot(d1); ylabel('d1'); axes(handles.axes7); [a2 d2]=dwt(a1,'db5'); plot(d2); ylabel('d2'); axes(handles.axes6); [a3 d3]=dwt(a2,'db5'); plot(d3); ylabel('d3'); axes(handles.axes5); [a4 d4]=dwt(a3,'db5'); plot(d4); ylabel('d4'); axes(handles.axes4); [a5 d5]=dwt(a4,'db5'); plot(d5); ylabel('d5'); axes(handles.axes3); [a6 d6]=dwt(a5,'db5'); plot(d6); ylabel('d6'); axes(handles.axes2); plot(a6); ylabel('a6'); 72

The following figures show the output wave forms of preprocessing and morphology feature extraction. Figure 4.7: Output Screen Demanding the Signal Number from User Figure 4.8: Resulted Signal of Record 100 after Preprocessing & DWT 73

Then the P-QRS-T points are constructed from the normalized variances of detail coefficients of the DWT which belongs to the related scales. According to the following procedure the points are constructed. In order to detect the peaks, specific details of the signal were selected. R peaks are the Largest amplitude points which are greater than threshold points are located in the wave. Those maxima points are stored and the R-R interval is determined. Their mean value is found which is used to find the portion of the single wave. The Q and S points are found as local minimum points before and after R wave. Calculating the distance from zero point or close zero left side of R peak within the threshold limit denotes Q peak. The onset is the beginning of the Q wave and the offset is the ending of the S-wave. Normally, the onset of the QRS complex contains the high-frequency components, which are detected at finer scales. Calculating the distance from zero point or close zero right side of R peak within the threshold limit denotes Q peak. Based on the PR interval and QT interval the P and Q points are determined respectively. Figure 4.9: Detected P-QRS-T Features for Signal 100 74

B. Higher-order Statistics and AR Modeling Additional statistical data will be utilized for ECG signal feature detection. For this purpose this research work proposed a complete procedure to extract temporal features using third order cumulant based AR modeling. The main problem in automatic ECG beat recognition and classification is that related features are very susceptible to variations of ECG morphology and temporal characteristics of ECG. In the study [96] the set of original QRS complexes typical for six types of arrhythmia taken from the MIT/BIH arrhythmia database, there is a great variations of signal among the same type of beats belonging to the same type of arrhythmia. Therefore, in order to solve such problem, this approach will rely on the statistical features of the ECG beats. For this purpose, third-order cumulant has been taken into account, which can be determined (for zero mean signals) as follows!" (4.2) # $!$" (4.3) % $&!$&" ' &'$' $ &' (4.4) ' & $' where E represents the expectation operator, and k, l, and m are the time lags. In this phase, third-order cumulant of selected ECG beats is used. Normalized ten points represents the cumulant evenly distributed within the range of 25 lags. Each succeeding samples of a signal as a linear combination of previous samples, that is, as the output of an all-pole IIR filter is modeled by linear prediction. This process locates the coefficients of an n th order auto-regressive linear process that models the time series x as '()''(*')'+ '('' (4.5) 75

where x represents the real input time series (a vector) and n is the order of the denominator polynomial a(z). In the block processing, autocorrelation method is one of the modeling methods of all-pole modeling to find the linear prediction coefficients. This method is as well called as the Maximum Entropy Method (MEM) of spectral analysis. The following commands are used for temporal feature extraction from preprocessed ECG signal using AR modeling and third order cumulant. Inputs to the function are x-input signal vector, p-the optimal AR model order, Fs-sampling frequency.this part of the code to determine the AR parameters. % Spectrum(f)=e(L)/ 1+A(L,1)*exp(-j*2*Pi*f/Fs)+ %...+A(L,L)*exp(-j2*Pi*f*L/Fs)^2 for i=1:nfreq den=0; for k=2:order+1 den=a(k)*exp(-j*2*pi*(i-1)*(k-1)/nfreq)+den; end power(i)=e(order)/abs(1+den)^2; end freq=0:fsamp/nfreq:(nfreq/2-1)*fsamp/nfreq; function[a,e,k]=ar(x,p,fs) A=zeros(p+1,p+1); K=zeros(1,p); E=zeros(1,p); N=length(y); % y is a raw vector % initialization Rxx=(y*y )/N; ef=y; % ef(n)=y(n) eb=y; % eb(n)=y(n) L=1; DEN=y(2:N)*y(2:N) +y(1:n-1)*y(1:n-1) ; Num=y(2:N)*y(1:N-1) ; K(1)=2*Num/DEN; %K(L)=-R(L) A(1,1)=-K(1); 76

The outputs are the E(1)=Rxx*(1-K(1)^2); ef(2:n)=y(2:n)-k(1)*y(1:n-1); eb(1:n-1)=y(1:n-1)-k(1)*y(2:n); % Calculation for L=2:p Num=ef(L+1:N)*eb(1:N-L) ; Den=ef(L+1:N)*ef(L+1:N) +eb(1:n-l)*eb(1:n-l) ; K(L)=2*Num/Den; E(L)=E(L-1)*(1-K(L)^2); A(L,L)=-K(L); for j=1:l-1 A(L,j)=A(L-1,j)-K(L)*A(L-1,L-j); end; efm=ef; ebm=eb; ef(l+1:n)=efm(l+1:n)-k(l)*ebm(1:n-l); eb(1:n-l)=ebm(1:n-l)-k(l)*efm(l+1:n); end; B(2:p+1,2:p+1)=A(1:p,1:p); B=zeros(p+1,p+1); B(:,1)=ones(p+1,1); B(2:p+1,2:p+1)=A(1:p,1:p); A=B(p+1,:); % End A: AR parameters matrix, the pth row is the final set of p AR parameters i.e. A=[1 A1 A2...Ap]; e(n)=y(n)+a1*y(n-1)+a2*y(n-2)+... +Ap*y(n-p); E: error variance vector=[e(0),e(1),e(2),...,e(p)]; K: a raw vector of reflection coefficients at each calculating step (from 1 to order p) Hence, the noise components in ECG signal are removed in preprocessing phase using morphology filter. From the preprocessed signal, P-QRS-T points are constructed using DWT. Temporal features of the preprocessed ECG signal are extracted using third order cumulant based AR modeling. These two feature sets will construct the input vectors for the classifiers. 77