Analisis Perbandingan Performansi Denoising Sinyal Ekg Menggunakan Metode Discrete Wavelet Transform Dan Adaptive Filter

Muhammad Akhyar Ghifari, Rita Magdalena, R Yunendah Nur Fu'adah

Abstract

Abstrak Denoising merupakan metode unuk menghilangkan noise pada sinyal EKG. Noise yang terdapat pada sinyal EKG tersebar pada rentang yang sama dengan frekuensi yang sama dengan sinyal EKG, sehingga metode filter biasa tidak mampu menghilangkan noise tersebut[2] . Dalam ujicoba denoising pada tugas akhir ini, penulis menggunakan perbandingan metode antara adaptive filter dan discrete wavelet transform (DWT). Dimana metode DWT melakukan denoising sinyal dengan menggunakan beberapa metode basis wavelet seperti Haar, Debuchies, Symlet dan Bior dan thresholding dengan metode soft atau hard thresholding. Sedangkan untuk adaptive filter sendiri dengan menggunakan metode KALMAN, Least Mean Square (LMS), dan Recursive Least Square (RLS). Ujicoba dilakukan dengan memberikan 4 noise yang berbeda yaitu Additive White Gaussian Noise (AWGN), MUSCLE ARTIFACT (MA), ELEKTRODE MOTION ( EMM ) dan BASELINE WANDER ( BW ) untuk masing masing metode DWT dan adaptive filter. Berdasarkan hasil pengujian, denoising terbaik yang dilakukan oleh metode Discrete Wavelet Transform untuk Additive White Gaussian Noise ( AWGN ) adalah dengan Basis Wavelet = DB 12 dengan Threshold Method = SURE dengan HARD THRESHOLD dan Level Dekomposisi = 2 dengan nilai MSE = 0.000498516 dan SNR = 28.12125292 dB. Sedangkan untuk denoising terbaik yang dilakukan oleh metode adaptive filter untuk Additive White Gaussian Noise ( AWGN ) adalah dengan metode LMS dengan nilai MSE = 0.000273995 dan SNR = 30.68395146 dB. Jika dibandingkan dari hasil diatas maka metode terbaik ditunjukkan oleh metode adaptive filter. Kata kunci: Elektrokardiogram (EKG), Denoising, Discrete wavelet Transform, Adaptive Filter. Abstract Denoising is a method to remove noise on ECG signals. The noise present in the ECG signal is spread over the same range with the same frequency as the ECG signal, so the usual filter method can not remove the noise. [2] In the denoising test in this final project, the writer uses a comparison method between adaptive filter and discrete wavelet transform (DWT). Where the DWT method of denoising the signal by using some wavelet base method such as Haar, Debuchies, Symlet and Bior and thresholding with soft or hard thresholding method. As for adaptive filter itself by using the method KALMAN, Least Mean Square (LMS), and Recursive Least Square (RLS). The experiments were conducted by giving 4 different noise like ADDITIVE WHITE GAUSSIAN NOISE (AWGN), MUSCLE ARTIFACT (MA), ELEKTRODE MOTION (EMM) and BASELINE WANDER (BW) for each DWT and adaptive filter method. Based on the test results, the best denoising performed by Discrete Wavelet Transform method for Additive White Gaussian Noise (AWGN) is with Wavelet = DB 12 with Threshold Method = SURE with HARD THRESHOLD and Decomposition Level = 2 with MSE value = 0.000498516 and SNR = 28.12125292 dB. While for best denoising done by adaptive filter method for Additive White Gaussian Noise (AWGN) is by LMS method with value of MSE = 0.000273995 and SNR = 30.68395146 dB. When compared from the above results then the best method is shown by adaptive filter method. Key words: Electrocardiogram (ECG), Denoising, Discrete wavelet Transform, Adaptive filters.

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