Denoising of ECG signals using FIR & IIR filter: A performance analysis

2Citations
Citations of this article
47Readers
Mendeley users who have this article in their library.

Abstract

Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.

References Powered by Scopus

Adaptive Filters

1238Citations
261Readers
Get full text
133Citations
65Readers
Get full text
Get full text

Cited by Powered by Scopus

Get full text

ECG Denoising: Evaluating the Effectiveness of Different Algorithms

1Citations
1Readers
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Saxena, C., Sharma, A., Srivastav, R., & Gupta, H. K. (2018). Denoising of ECG signals using FIR & IIR filter: A performance analysis. International Journal of Engineering and Technology(UAE), 7(4.12 Special Issue  12), 1–5. https://doi.org/10.14419/ijet.v7i4.12.20982

Readers over time

‘19‘20‘21‘22‘23‘24036912

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 8

57%

Professor / Associate Prof. 2

14%

Lecturer / Post doc 2

14%

Researcher 2

14%

Readers' Discipline

Tooltip

Engineering 19

76%

Computer Science 4

16%

Chemistry 1

4%

Physics and Astronomy 1

4%

Save time finding and organizing research with Mendeley

Sign up for free
0