Abstract

The ECG signal contain base line wander noise(0.5 to 0.6Hz), breathing noise (5Hz), power line interference(50Hz) and EMG noise(above 100Hz).Electrode Motion artifact noise can be reduced by minimizing movement made by the patient. High pass filter can be used to remove base line wander noise with cutoff frequency of 0.6 Hz. High pass filter can be used to remove breathing noise with cutoff frequency of 6 Hz.50 Hz power line interference can be removed using band stop filter. QRS detection is done using differentiation method. Scilab. is used for performing signal processing task of removing common noise in ECG signal. SNR of Input signals with base line wander noise, breathing and PLI noise when passed to FIR(high pass and band stop filter order 51) shows improvement in output SNR.

References

• Sattar Y, Chhabra L. Electrocardiogram. [Updated 2020 Jul 31]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK549803/
• Francis J. ,ECG monitoring leads and special leads, Indian Pacing and Electrophysiology journal, Issue-3 ,Volume-16, 2016
• Zègre-Hemsey, Jessica K et al. “Cardiac Monitoring in the Emergency Department.” Critical care nursing clinics of North America vol. 28,3 (2016): 331-45. doi:10.1016/j.cnc.2016.04.009
• Institute of Medicine (US) Division of Health Sciences Policy. Medical Education and Societal Needs: A Planning Report for the Health Professions. Washington (DC): National Academies Press (US); 1983. Chapter 4, THE PHYSICIAN'S ROLE IN A CHANGING HEALTH CARE SYSTEM. Available from: https://www.ncbi.nlm.nih.gov/books/NBK217690/
• K Manivel, R samson Ravindran”Noise removal for baseline wander and Power line in ECG signals”, IJAREEIE, Issue-2, Volume-4, 2015
• T. Aarthi, G. Sasi, P. Subapradha1, B. Subathra Thangam1, Ch. Sai Sita Sri, V. Elamaran1” A tutorial on digital filtering using discrete fourier transform with scilab” ,International Journal of Pure and Applied Mathematics, Special Issue on mathematical modeling of Engineering problems,, Issue-15, Vol.-119,2018
• De Pinto V,“Filters for the reduction of baseline wander and muscle artifact in the ECG”, J Electrocardiol. 1992; 25 Suppl: 40-8.
• Pan, J.; Tompking, W.J. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 1985, 32, 230–236.[CrossRef] [PubMed]
• Hamilton, P.S.; Tompkins, W.J. Quantitative investigation of QRS detection rules using MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 1986, 12, 1157–1165. [CrossRef]
• He, R.; Wang, K.; Li, Q.; Yuan, Y.; Zhao, N.; Liu, Y.; Zhang, H. A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization. EURASIP J. Adv. Signal Process. 2017,2017. [CrossRef]
• Lastre-Domínguez C, Shmaliy YS, Ibarra-Manzano O, Munoz-Minjares J, Morales-Mendoza LJ. ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing. Biomed Res Int. 2019;2019:2608547. Published 2019 Feb 20. doi:10.1155/2019/2608547.
• Christov II, Daskalov IK. Filtering of electromyogram artifacts from the electrocardiogram. Med Eng Phys. 1999 Dec; 21(10):731-6. doi: 10.1016/s1350-4533(99)00098-3. PMID: 10717553.