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International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014 Detection and Classification of Premature Ventricular Contraction using Cross Wavelet Transform R.Sunil kumar 1 M.Suchetha 2 1 PG Student, Department of Electronics And Communication, Vellammal Engineering College, Anna University, Chennai 2 Assistant Professor, Department of Electronics And Communication, Vellammal Engineering College, Anna University, Chennai ABSTRACT Premature ventricular contraction(PVC) is a cardiac problem generated with improper heart beat. In this paper a method using cross wavelet transforms(XWT) and the wavelet coherence(WCOS) are used to identify the patient with PVC disorder. Although many earlier scholars have developed many algorithms for PVC classification they have achieved efficiency less than 93% The proposed method analyses the Electrocardiogram(ECG) signal using XWT and the parameters are extracted from the Wavelet Cross Spectrum(WCS) and WCOS which are obtained from the XWT. The pathologically varying pattern from normal ECG to the abnormal ECG identifies the presence of PVC. The waveforms are selected from MIT/BIH Arrhythmia database for evaluation and a detection rate of true PVC close to 98% is obtained. The ECG signal analysis remains the crucial task for the detection of cardiac arrhythmia. Several methods has been proposed by various scholars. the most of the algorithm are based on time domain features[6], ECG morphology and heart beat interval features[7], principle component analysis(PCA)[8], hidden Markov models[9]-[11], self organizing maps[12], wavelet and filter banks[13][14], statistical classifier[15] and neural networks’[16][17]. Among this most of the work is mainly concentrated on the PVC detection. In the previous discussed literature the two main drawbacks discussed are problem of finding efficient feature sets and spotting out the optimum architecture which has a unique solution. INDEX TERMS- Cross Wavelet Transforms(XWT), Premature ventricular contraction(PVC), wavelet coherence(WCOS), wavelet cross spectrum(WCS), Electrocardiogram(ECG) I. INTRODUCTION Fig-1 Representation of a typical waveform. Cardiovascular diseases(CVD) has became the great threat for the people to cause death in the fast developing world. The death rate due to CVD is 1 in every 2.8 deaths[1]. Therefore it is very essential to detect this CVD efficiently and start treatment but the detection of this CVD in early stage is very tedious process. The heart beat in the cardiac cycle of each individual can be recorded in the ECG waveform. The CVD can be identified by analysing the recorded ECG waveform[2],[3]. ECG gave a clinical information regarding the heart beat rate, morphology and the proper functioning of heart at cheap rate and non-invasive test[4]. Most of the ECG analysis is used for the cardiac arrhythmias such as PVC. The immediate detection and treatment is very important for the patient with CVD because PVC's results from irritated ectopic foci in the ventricular area of the heart. These foci cause PVC which is independent of paceset by the sino-atrial(SA) node[5]. PVC beats occurs when ventricles starts to pumps prematurely or earlier to atria. During this period in ECG waves the QRS complex region will be expanded and P-waves will be absent. ISSN: 2249-2615 II. ANTICIPATED SYSTEM The combination of waveform shape and timing interval classification features gives better classification results [11], which means the efficient classification of arrhythmic beat are timing property dependent rather than on waveform shape. In this paper the cross wavelet transform(XWT) is applied to the reference sample and the test sample from which the spectral characters WCS and WCOS are obtained. First the R-peak was detected for beat segmentation followed by denoising and then R-peaks are registered before they undergone into further processing. The segmented heartbeat is then normalized and interpolated with respect to time. In this paper the normal case is taken as a reference sample and the ECG with PVC is taken as a test sample. The classification is done using WCS parameters. An iterative based mathematical formula but not an optimal one is used to extract the parameters from WCS and WCOS. III. CROSS WAVELET TRANSFORM(XWT) The two time series function x(n) and y(n)when it is applied with Cross Wavelet Transform it can be defined as http://www.ijpttjournal.org Page 8 International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014 ---- (1) where * denotes the complex conjugate. the cross power wavelet can be defined as the modulus of the above equation. the local relative phase between the two time series function x(n) and y(n) can be found from the argument of equation (1), --- (2) --- (3) The cross wavelet power can be obtained from the above equations and the coherence of two time series can be defined from the XWT in its time frequency space[18]. The WCOS of two time series can be defined as --- (4) Here S is the smoothing operator and ‘s’ is a scale and it is IV. MATERIAL ROUTINE AND PROGRESSIVE shown in fig-2 the WCS and WCOS are obtained from the XWT of reference and sample signal. These WCS and WCOS are analyzed to detect the presence of PVC. The mother wavelet used here is Daubechies wavelet since there is an existence of similarity between morphological structure and QRS complex. The time scale relationship between the two signals can be obtained from XWT. For the analysis 512 scales are considered. The region of interest(ROI) spectral component can be obtained from the resultant WCS. The morphological similarity in QRS complex and time difference can be easily extracted from this analysis. V. PARAMETER ESTIMATION The morphological features are registered from the WCS and WCOS matrices which are obtained from the resultant XWT. This section deals with two procedures for parameter estimation. Reference and sample data (ECG signal from MIT-BIH database) Denoising 1. Data All the ECG signal used in this paper has been selected from MIT-BIH Arrhythmia database, which was created in 1980 as a standard reference for arrhythmia detector[19]. The database is comprised of 48 files each containing 30-minutes ECG segments selected from 24 hours recording of 47 different patients. The lead V1 or ML1 is used here for analysis. R-Peak detection (Segmentation) Normalization with respect to Interpolation 2. Denoising And R-Peak Registration. In order to increase the efficiency of the algorithm the ECG signal must be denoised before it is subjected to further analysis. The basic DWT based denoising is used in this paper and the mother wavelet used here is Daubechies wavelet and the feature extraction is carried out by the method developed in [20]. Reference Sample CWT 3. Normalization And Interpolation. CWT XWT The R-peak has to be registered first, once it has been registered then it must be normalised with respect to time. In order to get the equal number of beat rate in one second it must be interpolated based on FFT technique. The interpolation has to done because in the sample data the heat beat rate will vary from one cardiac cycle to another. WCS WCOS Parameter Estimation 4. XWT Analysis On ECG Beat. The similarity between two signals can be found out by cross correlation. The various characters of the test sample are studied and implemented by applying CWT to the reference and test sample. As ISSN: 2249-2615 Test Sample Classification & Result Fig-2 Flow chart for the progressive algorithm http://www.ijpttjournal.org Page 9 International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014 5.1. Selection Of Scales --- (9) This selection is done from the WCS. It is done to know the tangible duration of the significant analysis for further analysis to establish a time scale relationship. This can be expressed as --- 10) The comparison list is stated in the below table --- (5) The variable Scales(S) represents the total WCS value at each scale over the entire ECG beat. The parameter is extracted in the P-Wave region and QRS complex region. 5.2. Extraction Of Parameters This extraction is based on the WCS and WCOS matrices from the selected range of scales s1 to s2 over P-Region and QRS complex region. the parameter can be extracted from the following relations --- (6) --- (7) The equation (6) represents the sum of WCS and equation (7) represents the sum of WCOS. Different sets of parameters are concidered with respect to R1 and R2, and then the analysis is done on the sample signal obtained from the lead ML2. VI. Exposure Scheme suggeste d by Time domain Statistics Moody et al [6] ----- 91.0 74.51 in % 89.46 97.36 PCA Moody et al [7] --- Hidden Markov model Cheng et al[9] Andreao et al[11] Alfonso et al[14] my self 93% 93.12 92.73 92.85 94.76 96.92 95.26 94% 99.79 99.76 --- 99.59 99.56 98% 97.3 98.8 Filter Bank Cross Wavelet Recital Rate in % Table-1 comparison of accuracy with existing method. CLASSIFICATION The classification for the presence or absence of the PVC can be done by checking the occurrence of P-Region and the type of PVC can be classified by setting a threshold point. Different ways are available for the selection of threshold point but in this paper centroid points are chosen from the WCS and WCOS matrices. The classification algorithm developed here is the combination of threshold point with the Scales(R1) and Scales(R2). The accuracy which is obtained from this analysis is 98% and the tool used for simulation is MATLAB. VII. SIMULATION RESULTS The sample signal is taken from MIT-BIH Arrhythmia database and the results are compared with existing methods. Accuracy - It is the major parameter in obtaining the overall success of the proposed method. This can be defined as Fig-3 Comparision chart for accuracy and statistical measures with the existing method. The lowest accuracy value is taken as 85% for plotting the graph. --- (8) VIII. Where NB denotes the total number of beats and 'e' denotes the total number of classification errors. Sensitivity And Specificity - These are the statistical measures of the performance of a binary classification test, also known as statistics as classification function. ISSN: 2249-2615 CONCLUSION In this paper the presence of PVC can be spotted out easily and efficiently using XWT and examining the CWT of both reference and sample signal. A Daubechies wavelet is used as a mother wavelet for initial analysis and further analysis are http://www.ijpttjournal.org Page 10 International Journal of P2P Network Trends and Technology (IJPTT) – Volume 7 – April 2014 taken with respect to WCS and WCOS spectrogram of normal and test data. the classification accuracy of this method is 98% this result paves the way for further analysis to be taken place with 12 lead type and to spot the other type of CVD's. 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