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Ch :[1] Thm hnh nh phn internet[2] Rt ra kt lun gia hnh tim vi bnh l[3] Lm thm bo co v phn internet, attach vo ba thuyt trnh[4] Test khong cch ti a l bao nhiu[5] Tm tt kt qu 1 paper ting vit , ting anh[6] Thm mu v background phn slide thuyt trnh [DONE]

X l tn hiu s bng cc phng php khc nhau:[1] phng php dng b lc IIR: u im: d thit k, b lc IIR c bc cng ln th cng trit nhiu t tn hiu rt ttKhuyt im: Thi gian p ng rt chm, tng thi gian lc, tn b nh, v khng th lc c tn hiu khng tuyn tnh[2] Phng php dng b lc thch nghi:u im: thi gian p ng lc nhiu nhanh v sai s li bKhuyt im: cn bit thng tin c tnh v c tn hiu ln nhiu [3] phng php lc trung bnh theo thi gian:Khuyn im: cn rt nhiu khung theo thi gian loi nhiu.In recent years, discrete wavelettransforms based thresholding is used to resolve the limitations on efficient noise removal fromECG signals using above mentioned filtering methods [11]. This method does not introduceany artificial information to the original signal and it independently generates the thresholdvalue based on the signal attributes [12]. However, selection of appropriate wavelet function,thresholding methods and thresholding rule play an important role in signal denoising[11].There are several types of wavelet functions are available to denoise the signals and to extractthe efficient statistical and geometrical features for further applications. Some of theresearchers considered to select the mother wavelet function based on: (i) eyeball inspection,(ii) correlation between the signal of interest and original signal, and (iii) based on thecumulative energy [13] . Genetic algorithm based mother wavelet and thresholding selectionalso considered to denoise the signal and it is the complex algorithm for the mother waveletselection and may require more computation time that not included in detail [11].In this work, the DWT based denoising was performed to remove the three different noisesfrom ECG signal. Three different wavelet functions and four thresholding rules wereconsidered to analyze the efficiency on noise removal from ECG signals. The organization ofthis paper is given as follows: section 2 describes the implementation of DWT based denoisingof ECG signals using thresholding methods, section 3 discusses the research methodology,section 4 presents the computational performance measure, section 5 discusses the results ofthis work and finally conclusion is given in section 5.