ECG Parameter Extraction Algorithm using (DWTAE) Algorithm

Accurate measurement of ECG parameters is an important requirement of quantitative ECG analysis, particularly if the results of ECG signal analysis are to be used for clinical purposes. However, the accuracy with which these parameters can be measured depends mainly on the accuracy of the analysis algorithm. This paper presents a new method for the accurate estimation of ECG parameters, contaminated by instrumentation and biological noise, this method is based on three processes. First step is removing low-frequency components using DWT transformation techniques. Second step is to denoise the signal using the same previous approach DWT; signal denoising using the DWT consists of three successive procedures, namely, signal decomposition, thresholding of the DWT coefficients, and signal reconstruction. The final step is extract the ECG features (PQRST) from the processed signal. The simulated method is compared to traditional ECG analysis algorithm techniques such as Saxena, So, MOBD. Using real ECG signal records from the MIT/BIH arrhythmia database as benchmark, The MIT/BIH arrhythmia database contain twelve half-hour ECG recordings and 3 half-hour recordings of noise typical in ambulatory ECG recordings. The ECG recordings were created by adding calibrated amounts of noise to clean ECG recordings from the MIT-BIH Arrhythmia Database, Overall performance of the proposed method is evaluated and compared to the performance of the traditional estimation techniques provided. The ECG analysis is done using MATLAB the lead academic software which is widely used in signal analysis and academic institutions and much more facilitating features. Moreover, it provides simple control in programming.