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Peak Detection in ECG and ABP Signals using Empirical Mode Decomposition DEPARTMENT OF ELECTRONICS & COMMUNICATIONSHRI RAM MURTI SMARAK COLLEGE OF ENGINEERING AND TECHNOLOGY,BAREILLYSUBMITTED TO: SUBMITTED BY:Mr.VivekYadavShreyas SinghPiyushChaurasiyaAtal Singh YadvGaurav Singh
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INTRODUCTION Automatic beat detection algorithms are extremely important for various biomedical signalprocessing applications. These types of algorithms are mostly used for R-peak detection in ECG. TheECG signal is a recording of electrical activity of heart. A single ECG cycle consists of P, Q, R, S, and Twaves. The QRS complex and especially R-peak detection is the most prominent feature in the ECGsignal and its accurate detection forms the basis of extraction of other features and parameters fromECG signal. Since the QRS complex varies with different cardiac health conditions, therefore efficientand automatic detection of QRS complex and R-Peak is essential for reliable health conditionmonitoring.Although many algorithms have been developed during the last five decades for accurate andreliable detection of R-peaks in the ECG signal indicating high percentages of correct detection, thereare only a few publications that describe algorithms to detect features in pressure signals [10]–[12].The earlier QRS complex detection algorithm involve a preprocessor stage, where the ECG signal istransformed to accentuate the QRS complex, and a decision stage, where a QRS complex is detectedusing thresholding, yielded 99.3% detection accuracy [1]. This was further improved to a detectionaccuracy of 99.67% [2]. A QRS detection algorithm using hardware filter banks was proposed whichreported sensitivity of 99.59 % and positive predictivity of 99.56 % against the MIT-BIH ArrhythmiaDatabase [5]. A wavelet transforms based QRS detection algorithm was proposed which reported0.15 % false detections [7]. A new wavelet based QRS detection algorithm was developed whichyielded very high detection accuracy of 99.99% [6].There are numerous current and potential applications for Pressure beat detection algorithms. Manypulse oximeters perform beat detection as part of the signal processing necessary to estimateoxygen saturation. Identification of the pressure components is necessary for some methods thatassess the interaction between respiration and beat-by-beat ventricular parameters and themodulation effects of respiration on left ventricular size and stroke volume [13]. In the present worka beat detection algorithm for ECG and ABP signals based on empirical mode decomposition hasbeen proposed. The proposed beat detection algorithm was tested on different data records ofFantasia database, Self- recorded signals and MIMIC database [9]. The algorithm was implementedin MATLAB.
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METHODOLOGYEmpirical Mode Decomposition (EMD) has been recently introduced by Huang for adaptivelydecomposing signals in a sum of ―well-behaved‖ AM-FM components [15]. The EMD is defined by aprocess called sifting. It decomposes a given signal x(t) into a set of AM–FM components, calledIntrinsic Mode Functions (IMF). Using this technique K modes dk(t) and a residual term r(t) areobtained and expressed by: x(t) = k=1,2,…,K. (1) The EMD algorithm is summarized as below:1. Start with the signal d1(t) = x(t), k = 1. Sifting process hj(t) =dk(t) , j = 02. Identify all local extrema of hj(t). 3. Compute the upper (EnvMax) and the lower envelopes(EnvMin) by cubic spline lines interpolation of the maxima and the minima. 4. Calculate the mean ofthe lower and upper envelopes,3. Compute the upper (EnvMax) and the lower envelopes (EnvMin) by cubic spline lines interpolationof the maxima and the minima. 4. Calculate the mean of the lower and upper envelopes,4. Calculate the mean of the lower and upper envelopes,m(t) = (EnvMin(t)+ EnvMax(t)5. Extract the detail h j+1(t) =h j(t) −m(t).6. If h j+1(t) is an IMF, go to step 7, else, iterate steps 2 to 5 up on the signal h j+1(t), j = j +1.7. Extract the mode dk(t) =h j+1(t).8. Calculate the residual rk(t) = x(t) −dk(t).9. If rk(t) has less than 2 minima or 2 extrema, the extraction is finished r(t) =rk(t). Else iterate thealgorithm from Step1 upon the residual rk(t), k =k +1.
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FLOWCHARTThe flowchart of the algorithm is shown in figure 1. The ECG /ABP signal is decomposedinto IMF’s using EMD technique as shown in figure 2.Figure1. Flowchart of the implemented algorithm
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Fine to coarse approximation are determined by adding the IMF’s according to the followingequationThe fine to coarse approximations are shown in figure 3.The signal f2c7 (t) = y(t) signal hasbeen used in further processing of the signal.
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