Transactions on Machine Intelligence

Transactions on Machine Intelligence

An Investigation on The performance of Infinite Impulse Response Filters in Denoising Electrocardiogram Signals

Document Type : Original Article

Authors
Faculty of Engineering, Jahrom University, Jahrom, Iran
Abstract
Electrocardiogram (ECG) signals play a vital role in the clinical assessment of cardiac function, enabling the diagnosis of various heart disorders and the monitoring of treatment outcomes. However, these signals are frequently contaminated by diverse sources of noise, including electromagnetic interference from urban environments (such as 50 Hz powerline noise), muscle activity (electromyographic signals), and even neurological signals. These unwanted signal components, collectively referred to as "noise," can significantly degrade the quality of the ECG waveform, complicating both visual inspection and automated analysis. To ensure the accurate interpretation of ECG recordings, it is essential to employ effective signal processing techniques that can suppress noise while preserving the integrity of diagnostically relevant features. In this study, we investigate and compare the performance of three Infinite Impulse Response (IIR) digital filters: Butterworth, Chebyshev Type I, and Chebyshev Type II. The primary objective is to attenuate the dominant 50 Hz interference commonly observed in urban clinical and research environments. Comprehensive simulations and tests on real ECG signals demonstrate that the Chebyshev Type I filter offers a particularly effective balance between sharp frequency selectivity and minimal signal distortion. Its performance in attenuating noise within both the passband and stopband makes it a favorable choice for preprocessing ECG data, thereby enhancing diagnostic accuracy in biomedical signal analysis applications.
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Volume 6, Issue 1
Winter 2023
Pages 10-15

  • Receive Date 07 December 2022
  • Revise Date 09 January 2023
  • Accept Date 05 March 2023