Denoising wrist pulse signals using variance thresholding technique

GC Suguna  lowast; ST Veerabhadrappa

doi:10.17485/IJST/v13i40.1625

Article

Denoising wrist pulse signals using variance thresholding technique

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i40.1625

Year: 2020, Volume: 13, Issue: 40, Pages: 4275-4286

Original Article

Denoising wrist pulse signals using variance thresholding technique

GC Suguna^1∗, 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:
Tel: 9611692222
[email protected]

Received Date:11 September 2020, Accepted Date:11 November 2020, Published Date:23 November 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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

References

Saxena S, Jais R, Hota MK. Removal of powerline interference from ECG signal using FIR, IIR, DWT and NLMS adaptive filter. 2019 International Conference on Communication and Signal Processing (ICCSP). 2019;p. 0012–0016. Available from: https://doi.org/10.1109/ICCSP.2019.8698112
Posada-Quintero HF, Chon KH. Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review. Sensors. 2020;20(2). Available from: https://dx.doi.org/10.3390/s20020479
Rakshit M, Das S. An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomedical Signal Processing and Control. 2018;40:140–148. Available from: https://dx.doi.org/10.1016/j.bspc.2017.09.020
Torres-Soto J, Ashley EA. Multi-task deep learning for cardiac rhythm detection in wearable devices. NPJ Digital Medicine. 2009;3(1):1–8. Available from: https://doi.org/10.1038/s41746-020-00320-4
Zhang D, Wang S, Li F, Wang J, Sangaiah AK, Sheng VS, et al. An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter. Applied Sciences. 2019;9(22). Available from: https://dx.doi.org/10.3390/app9224968
Bouny LE, Khalil M, Adib A. Performance analysis of ECG signal denoising methods in transform domain. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). p. 1–8.
Hazarika J, Kant P, Dasgupta R, Laskar SH. EEG Wavelet Coherence Based Analysis of Neural Connectivity in Action Video Game Players in Attention Inhibition and Short-term Memoryretention Task. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering). 2019;12(4):324–338. Available from: https://dx.doi.org/10.2174/2352096511666180821111536
Mandala S, Fuadah YN, Arzaki M, Pambudi FE. Performance analysis of wavelet-based denoising techniques for ECG signal. In: 5th International Conference on Information and Communication Technology (ICoIC7). Malacca City. p. 1–6.
Lahmiri S. Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthcare technology letters. 2014;16(3):104–109. Available from: https://doi.org/10.1049/htl.2014.0073
Dautov ÇP, Özerdem MS. Wavelet transform and signal denoising using Wavelet method. In: 26th Signal Processing and Communications Applications Conference (SIU). Izmir. p. 1–4.
Bruni V, Vitulano D. Signal and image denoising without regularization. In: 2013 IEEE International Conference on Image Processing. (pp. 539-542) IEEE. 2013.
Tibdewal MN, Mahadevappa M, Ray AK, Malokar M, Dey HR. Power line and ocular artifact denoising from EEG using notch filter and wavelet transform. In: 3rd International Conference on Computing for Sustainable Global Development (INDIACom). New Delhi. p. 1654–1659.
Chan HL, Tsai YT, Meng LF, Wu T. The removal of ocular artifacts from EEG signals using adaptive filters based on ocular source components. Annals of biomedical engineering. 2010;38(11):3489–99. Available from: https://doi.org/10.1007/s10439-010-0087-2
Yadav T, Mehra R. Denoising and SNR improvement of ECG signals using wavelet based techniques. In: 2nd International Conference on Next Generation Computing Technologies (NGCT). (pp. 678-682) 2016.
Jumah AA, Ahamad MG, Ali SA. Denoising of medical images using multiwavelet transforms and various thresholding techniques. 2013. Available from: https://doi.org/10.4236/jsip.2013.41003
Thakker B, Vyas AL, Farooq O, Mulvaney D, Datta S. Wrist pulse signal classification for health diagnosis. In: 4th International Conference on Biomedical Engineering and Informatics (BMEI). Shanghai. p. 1799–1805.
B’charri OE, Latif R, Elmansouri K, Abenaou A, Jenkal W. ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. BioMedical Engineering OnLine. 2017;16. Available from: https://dx.doi.org/10.1186/s12938-017-0315-1
Bui TD, Chen G. Translation-invariant denoising using multiwavelets. IEEE Transactions on Signal Processing. 1998;46(12):3414–3420. Available from: https://dx.doi.org/10.1109/78.735315
Chopra M, Kaur R, Garg N. Study and evaluation of denoising and baseline wandering of computerized wrist pulse signal using virtual instrument. In: 1st International Conference on Next Generation Computing Technologies (NGCT). (pp. 576-579) 2015.
Sun Z, Xi X, Yuan C, Yang Y, Hua X. Surface electromyography signal denoising via EEMD and improved wavelet thresholds. Mathematical Biosciences and Engineering. 2020;17(6).

Copyright

© 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).