An Investigation on the Performance Analysis of ECG Signal Denoising Using Digital Filters and Wavelet Family
Pinjala N Malleswari1, Ch. Hima Bindu2, K. Satya Prasad3
1Pinjala N Malleswari, Research Scholar, University College of Engg, JNTUK, Kakinada, AP, India.
2Ch.HimaBindu, Professor, ECE Department, QISCET, Ongole, AP, India.
3K. Satya Prasad, Rector, Vignan’s Foundation for Science, Technology and Research University, Guntur, AP, India.

Manuscript received on 09 April 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 166-171 | Volume-8 Issue-1, May 2019 | Retrieval Number: A2988058119/19©BEIESP
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Abstract: Electrocardiography is a technology used to identify the abnormalities in heart and noise free ECG data is often required for correct medication of cardiac disorders. Generally ECG signals are contaminated by noise and human artifacts during data acquisition. The denoising signal plays a major role in medical field. Electrocardiogram (ECG) signals represent important characteristics for diagnosing the disease or how the treatment works on the heart, which makes it necessary to design filters to weaken and eliminate these noises. This paper describes the denoising of ECG signal from baseline wander noise using digital filters and wavelet transform. The function of the filters has been tested on different cardiac signals. The results show that wavelet transform has the best performance in denoising ECG signals than digital filters such as IIR (Infinite Impulse Response) notch and window based FIR (Finite Impulse Response) filters. Finally the performance of the wavelet based approach is evaluated with SNR (Signal to Noise Ratio) value, PSNR (Peak Signal to Noise Ratio) value, Mean Square Error (MSE) value and Correlation Coefficient (CC) value and compared among various wavelet families. All simulations are carried out using MATLAB.
Index Terms: ECG Signal, Denoising, Wavelet Decomposition, Reconstruction, Digital Filters.

Scope of the Article: Measurement & Performance Analysis