Iaetsd a review on ecg arrhythmia detection

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Proceedings of International Conference on Developments in Engineering Research

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A Review on ECG Arrhythmia Detection based on DD-DW Transformation 1

B.Maheswari M .Tech Student

2

T.Nirmala M.Tech

Assistant professor

3

A.Rajani M.Tech

Assistant professor

Department of ECE, Annamacharya Institute of Technology and Sciences, Tirupati, India-517520. 1

mahisolmon@gmail.com nimmohan12@gmail.com 3 rajanirevanth446@gmail.com 2

Abstract—Machine learning of Electrocardiogram (ECG) is a core component in any of the ECG-based healthcare informatics system. Since the ECG is a nonlinear signal, the subtle changes in its amplitude and duration are not well manifested in time and frequency domains. Therefore, a machine-learning approach to screen arrhythmia from normal sinus rhythm from the ECG is proposed. The methodology consists of R-point detection using the double density discrete wavelet transformation (DD-DWT) decomposition, statistical validation of features. The average accuracy of classification is used as a benchmark for comparison. Support vector machine (SVM) kernel is used as classifier for better classification purpose. The proposed method will provide better results compared to state-ofart criteria like Signal quality indices (SQI) based feature extraction method. DD-DWT based ECG feature extraction is used in clinical purpose like intensive care unit (ICU) monitors for diagnosis of abnormalities in heart beat and is used for psychological analysis of human activities. Index Terms—Electrocardiogram (ECG), intensive care unit (ICU), signal quality.

I. INTRODUCTION In certain signal processing applications, like de noising, over complete transforms can offer a better tradeoff between performance and complexity, compared to critically sampled transforms. A distinguished member of the family of over complete discrete wavelet transforms (DWT) is the double density (DD) DWT [1], based on the filter bank shown in Figure 4. The input signal is split in three channels, each decimated by a factor of two. The signal on the first channel is processed by an identical filter bank.

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The DD-DWT [1] is expansive with a factor of two, compared to the critically sampled DWT arrhythmias based on their characteristics features extracted from ECG signals. Intended for Premature ventricular contraction (PVC) beat exposure RR interval ratio and power of beat is calculated. Dominant notched R wave, dominant S wave, QRS duration and direction of T wave are used for the detection of Left bundle branch block (LBBB) and Right bundle branch block ( RBBB). The rest of this paper is organized as follows: presents the ECG signal processing, ECG De noising, ECG parameter calculation, DD-DW Transformation, Feature Extraction and Selection. Describes the detection of arrhythmia. Finally, the summarizes the result & conclusion of this work. II. EXISTING APPROACH A. Pre-processing of ECGs Each channel of ECG was filtered to remove baseline wander and low frequency noise using a high pass filter with a cut-off at 1 Hz. QRS detection was performed on each channel individually using two open source QRS detectors (eplimited and wqrs). since eplimited is less sensitive to noise, we prefer eplimited detector. B. Signal quality indices Six signal quality indices (SQIs) were chosen based on earlier work and run on each of the m = 12 leads separately, producing 72 features per recording: 1. lSQI: The percentage of beats detected on each lead which were detected on all leads. 2. bSQI: The percentage of beats detected by wqrs that were also detected by eplimited.

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3. fSQI: The ratio of power P(5-20Hz)/P(0-fnHz), where fn=62.5 Hz is the Nyquist frequency. 4. sSQI: The third moment (skewness) of the distribution. 5. kSQI: The fourth moment (kurtosis) of the distribution. 6. pSQI: The percentage of the signal xm which appeared to be a flat line (dxm/dt < ÇŤ where ÇŤ =1mV). C. Support Vector Machine Classification In this section, a brief description of the two and multi-class SVM classification concept is reviewed. Support Vector Machines (SVMs) [17] are very popular and powerful in pattern learning because of supporting high dimensional data and at the same time, providing good generalization properties. Moreover, SVMs have many usages in pattern recognition and data mining applications, phoneme recognition, 3D object detection, image classification, bio-informatics etc. At the beginning, SVM was formulated for two-class (binary) classification problems. The extension of this method to multi-class problems is neither straight forward nor unique. DAG SVM is one of the methods that have been proposed to extend SVM classifier to support multi-class classification [17]. D. Binary Support vector machine formulation Let be a set of n training samples, where is an mdimensional sample in the input space. In a support vector machine, the optimal hyper plane is obtained by maximizing the generalization ability of the SVM. However, if the training data are not linearly separable, the obtained classifier may not have high Generalization ability, even though the hyper planes are determined optimally. To enhance linear separability, the original input space is mapped into a high-dimensional dot-product space called the feature space. Now using the nonlinear vector function that maps the m-dimensional input vector x into the l-dimensional feature space. E. Multi Class Support vector machine As described before, SVMs are intrinsically binary classifiers but the classification of ECG signals often involves more than two classes.

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In order to face this issue, a number of multiclass classification strategies can be adopted. III. PROPOSED CRITERIA A.ECG Signal Processing The proposed method includes processing and parameter calculation of ECG and then detection of cardiac arrhythmia using an algorithm developed in MATLAB 7.12 simulation tool. The algorithm is tested over MIT-BIH Arrhythmia database. B.ECG De noising ECG signals are usually corrupted by several noises like 50 Hz power line interferences, baseline wander and electro mayogram (EMG). Therefore, the signal needs to be preprocessed before applying any detection algorithm. Wavelet de noising and S- Golay Filter is used for removal of baseline wander and high frequency noise. ECG unfiltered data is passed through baseline wandering removal function, followed by wavelet based high frequency noise removal. The data is then smoothed. Further using SGolay filter. All the modules are implemented and simulated in MATLAB. C. ECG Parameter Calculation The purpose of the feature extraction process is to select and retain relevant information from original signal. The Feature Extraction stage extracts diagnostic information from the ECG signal. In order to detect the peaks, specific details of the signal are selected. The detection of R peak is the first step of feature extraction. R peak is detected by using PanTompkins algorithm. The intervals QRS, PR and QT are calculated by searching for corresponding onset and offset points in the wave. The separate logic was implemented for identifying P-onset, Q and S points, once R-peak was located using Pan-Tompkins algorithm. The window is selected around R-wave and the minimum of the points within this window are declared as Q and S points. In the differentiated signal ECG, a window of 155 ms is defined starting 225 ms before R-peak position. In this window, we search for maximum and minimum signal value. The P-wave peak is assumed to occur at the zerocrossings between maximum and minimum values within the selected window. Once P-wave peak is found, we proceed to locate waveform boundary-P-

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wave onset. Similarly T wave ms with abnormal morphology. Premature — i.e. occurs earlier than would be expected for the next sinus impulse. Discordant ST segment and T wave changes. There are five different types Of PVC, first Bigeminy every other beat is PVC, second Trigeminy every third beat is a PVC, third Quadrigeminy every fourth beat is a PVC, forth Couplet two consecutive PVCs and last Triplet three consecutive PVCs. The main characteristic of PVCs is its premature occurrence. This characteristic is measured by relating the RR interval lengths of heart cycles adjacent to the PVC. In case of a PVC, these lengths should be notoriously different .The method for classifying the abnormal complexes from the normal ones is based on the concepts of RR interval ratio of detected R peaks and energy analysis of ECG signal.

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In particular, the considered beat types refer to following classes: normal sinus rhythm (N), right bundle branch block (RB), left bundle branch block (LB), and paced beat (P). In Figure 2, sample of four N, RB, LB, and P beats are noticeable. ii) Feature Description For each signal nineteen temporal features such as RR interval, PQ interval, PR interval, PT interval and three morphological features are recognized. These features are manually extracted for each beat and put into a separate vector. Each vector is tagged with one the four possible labels N, P, LB, RB.

D. Feature Extraction and Selection In this section explain the characteristics of the extracted feature from the ECG signals and the procedure designed for the extraction. Figure 1, presents the block diagram of the proposed arrhythmia classification.

Fig 3: sample features, ST interval, TP interval and RR interval

Fig.1.Block diagram of proposed arrhythmia classification

The three morphological features by computing the maximum and the minimum values of a beat in ECG signal. Signals of each beat are scaled, using the following formula, such that the range of every signal is between zero and one.

i) Dataset Description In this experiments were conducted on the ECG data as the basic signal for classification. In recent researches, the annotated ECG records, available at the MIT-BIH arrhythmia database, have been widely used for the evaluation of the performance of different classifiers. The database has 48 records with each record being an ECG signal for the duration of 30 minutes.

Fig 2: Sample signal of Normal, Paced, LBBB and RBBB

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The minimum and maximum voltages between the first and the second R feature is computer first and the normalization action is performed [0 1]. As mentioned before, we considered percent that are higher than 0.2, 0.5 and 0.8 as three features. Six of the 22 features called basic features are: R1, S, T, P, Q, R2 and the rest are called derived features. The derived features are calculated using the basic features via a semiautomatic procedure. We suggest first and second R point to expert using an algorithm based on maximum-minimum. Then the expert distinguishes appropriate points(R, S, T, P, Q, and R).

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Proceedings of International Conference on Developments in Engineering Research

E.Double Density Wavelet Transform 1)1-D Double Density DWT (DD) The DD was consisted of two stages of filter banks as shown in Fig. 4: (i)Analysis In the analysis filter banks, three filters were implemented and the original signals were downsampled by 2 in order to decompose the signals into three sub-bands. The low frequency sub-band c(n) was produced by low pass filter h (-n) and the two 0

high frequency sub-bands d (n) and d (n) were 1

2

produced by high pass filters h (-n) and h (-n). 1

2

(ii) Synthesis The synthesis filter banks were the inverse of analysis filter banks where the three sub-bands were up-sampled by 2, filtered by the high pass filter h (n) 0

and the two low pass filters h (-n) and h (-n). The 1

2

filtered signals were combined to form the output signal x(n).

Fig.4.Filter bank diagram of Double Density DWT 2) Double Density Complex DWT (DDC) The input data, were processed by two parallel iterated filter banks h (n) and g (n) where i = 0, 1, 2. i

i

The real part of a complex wavelet transform [2] was produced by the sub-band signals of the upper DWT and the imaginary part was produced by the lower DWT.

Fig.5.Filter bank diagram of Double Density Complex DWT

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F.DETECTION OF ARRHYTHMIA A premature ventricular contraction (PVC) also known as a premature ventricular complex, ventricular premature contraction (complex or complexes) (VPC), is Broad QRS complex (≼ 120) range [0, 1]. Therefore, S SQI and k SQI were normalized by subtracting the mean and dividing by the standard deviation, with both of which computed on the training set. Sensitivity (Se) measures the proportion of poor quality signals that have been correctly identified as such. Specificity (Sp) measures the proportion of good quality signals that have been correctly identified as acceptable, and accuracy (Ac) corresponds to the proportion of signals that have correctly been classified. These statistical measures are calculated from the number of true positive (TP), true negative (TN), false positive (FP) and false negative (FN) with Se = TP/(TP+FN), Sp = TN/(FP+TN) and Ac = (TP+TN)/(TP+TN+FP+FN). The energy of ECG signal is calculated for each beat and RR interval ratio is also calculated. The threshold for energy is taken as 65% of maximum energy and for ratio 70% of maximum ratio value. If RR interval ratio and energy is less than threshold PVC beat is detected. IV. EXPERIMENTAL RESULTS To report the two examples of design. The SDP problem has been solved using the library SeDuMi. The optimization problem has been solved with the large scale version of the Matlab function fmincon, with default parameters, excepting TolCon (the tolerance for satisfying the constraints), which has been set. The tolerance Ǎ from has been set , which means that the PR constraints are satisfied with very good precision. The results obtained after 10 refinement iterations, although in the second example fewer iterations give approximately the same result. V. CONCLUSION The contribution of this is twofold. Firstly, the Hilbert transform [4] FIR approximation problem can be expressed as an SDP problem and solved reliably. Secondly, a complete algorithm to compute the second filter bank of a dual-tree double-density DWT. The algorithm comprises an initialization step based on SDP, followed by iterative refinement via nonlinear optimization.

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Previous method Using SQI Specificity

0.7500

Proposed method using DDDWT 0.92

Sensitivity

0.8000

0.87

Accuracy

0.7931

0.82

Precision

0.9524

0.97

REFERENCES [1] I.W. Selesnick, “The Double-Density Dual-Tree DWT,” IEEE Trans. Signal Processing, vol. 52, no. 5, pp. 1304–1314,May 2004. [2] N.G. Kingsbury, “Complex wavelets for shift invariant analysis and filtering of signals,” J. Applied and Computational Harmonic Analysis, vol. 10, no.3, pp. 234–253,May 2001. [3] I.W. Selesnick, R.G. Baraniuk, and N.G. Kingsbury,“The Dual-Tree ComplexWavelet Transform,”IEEE Signal Proc.Magazine, vol. 22, no. 6, pp. 123–151, Nov. 2005. [4] I.W. Selesnick, “Hilbert Transform Pairs of Wavelet Bases,” IEEE Signal Proc. Letters, vol. 8, no. 6, pp. 170–173, June 2001. [5] R. Yu and H. Ozkaramanli, “Hilbert Transform Pairs of Orthogonal Wavelet Bases: Necessary and Sufficient Conditions,” IEEE Trans. Signal Processing, vol. 53, no. 12, pp. 4723–4725, Dec. 2005. [6] B. Dumitrescu, “SDP Approximation of a FractionalDelay and the Design of Dual-Tree Complex Wavelet Transform,” IEEE Trans. Signal Proc.,2008, to appear. [7] B. Dumitrescu, Positive trigonometric polynomials and signal processing applications, Springer, 2007. [8] T.N. Davidson, Z.Q. Luo, and J.F. Sturm, “Linear Matrix Inequality Formulation of Spectral Mask Constraints with Applications to FIR Filter Design,” IEEE Trans. Signal Proc., vol. 50, no. 11, pp. 2702– 2715, Nov. 2002. [9] B. Alkire and L. Vandenberghe, “Convex optimization problems involving finite autocorrelation sequences,” Math. Progr. ser. A, vol. 93, no. 3, pp. 331–359, Dec. 2002.

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[10] B. Dumitrescu, I. T˘abus¸, and P. Stoica, “On the Parameterization of Positive Real Sequences and MA Parameter Estimation,” IEEE Trans. Signal Proc., vol. 49, no. 11, pp. 2630–2639, Nov. 2001. [11] Y. Genin, Y. Hachez, Yu. Nesterov, and P. Van Dooren, “Optimization Problems over Positive Pseudopolynomial Matrices,” SIAM J. Matrix Anal. Appl., vol. 25, no. 1, pp. 57–79, Jan.2003. [12] J.F. Sturm, “Using SeDuMi 1.02, a Matlab Toolbox for Optimization over Symmetric Cones,” Optimiza-tion Methods and Software, vol. 11, pp. 625–653,1999. [13] J. Gao, H. Sultan, J. Hu and W. W. Tung, “De noising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison,” IEEE Sig. Proc. Let., vol. 17, no. 3, 2010, pp. 237-240. [14] Fraser HSF, Blaya J. Implementing medical information systems in developing countries, what works and what doesn’t. AMIA Annu Symp Proc 2010;2010:232–236. [15] Waegemann CP. mHealth: the next generation of telemedicine? Telemed J E Health 2010;16(1):23–25. [16] Li Q, Mark RG, Clifford GD. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality 1. indices and a Kalman filter. Physiological measurement 2008;29(1):15–32. [17] Bishop CM. Pattern Recognition and Machine Learning. Springer Verlag, 2006. [18] Monasterio V, Clifford GD. Robust apnoea detection in the neonatal intensive care unit. Annals of Biomedical Engineering 2011;In Submission.

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