Indian Journal of Science and Technology
Year: 2020, Volume: 13, Issue: 40, Pages: 4275-4286
GC Suguna1∗, ST Veerabhadrappa2
1 Assistant Professor and Research Scholar, Department of Electronics and Communication, JSS Academy of Technical Education, Bengaluru, and Affiliated to Visvesvaraya Technological University, Belagavi, KA, India, Tel.: 9611692222
2 Associate Professor, Department of Electronics and Communication, JSS Academy of Technical Education, Bengaluru, and Affiliated to Visvesvaraya Technological University, Belagavi, KA, India
∗ Corresponding author:
Received Date:11 September 2020, Accepted Date:11 November 2020, Published Date:23 November 2020
Background/Objectives: Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. Methods/Statistical analysis: Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. Findings: Variance thresholding technique showed significant improvement in Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and reduction in Absolute Error (AE) and Root Mean Square Error (RMSE) compared to other thresholding methods. Novelty/Applications: Experimental results showed drastic improvement in PSNR and SNR retaining the pathophysiological information of the wrist pulse signal for future analysis.
Keywords: Wrist pulse; SNR; PSNR; AE; RMSE; wavelets
© 2020 Suguna & Veerabhadrappa. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).
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