Study and evaluation of denoising and baseline wandering of computerized wrist pulse signal using virtual instrument

Chopra, Manu; Kaur, Ramandeep; Garg, Nidhi

doi:10.1109/ngct.2015.7375186

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“…2.4 Evaluation parameters of the denoising techniques are (19) 1. Signal to Noise Ratio (SNR): SNR is the ratio of denoised wrist signal power to noisy signal power in decibels (dB).…”

Section: T Hrmentioning

confidence: 99%

Denoising wrist pulse signals using variance thresholding technique

Suguna¹

2020

IJST

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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.

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“…2.4 Evaluation parameters of the denoising techniques are (19) 1. Signal to Noise Ratio (SNR): SNR is the ratio of denoised wrist signal power to noisy signal power in decibels (dB).…”

Section: T Hrmentioning

confidence: 99%