COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES

Jarelh Galdos; Nikolai Lopez Colque; Angie Medina Rodirguez; Jorge Huarca Quispe; Jorge Rendulich; Erasmo Sulla Espinoza

doi:10.35784/acs-2024-10

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Published: Mar 30, 2024

DOI: https://doi.org/10.35784/acs-2024-10

DOI

https://doi.org/10.35784/acs-2024-10

Authors

Jarelh Galdos

jgaldosb@unsa.edu.pe

National University of San Agustín of Arequipa

https://orcid.org/0000-0001-6591-1866

Nikolai Lopez

nlopezcol@unsa.edu.pe

National University of San Agustín of Arequipa

https://orcid.org/0000-0002-5748-0717

Angie Medina

amedinarodr@unsa.edu.pe

National University of San Agustín of Arequipa

Jorge Huarca

jhuarcaq@unsa.edu.pe

National University of San Agustín of Arequipa

https://orcid.org/0000-0001-6300-5122

Jorge Rendulich

jrendulich@unsa.edu.pe

National University of San Agustín of Arequipa

https://orcid.org/0000-0003-1302-547X

Erasmo Sulla

esullae@unsa.edu.pe

National University of San Agustín of Arequipa

https://orcid.org/0000-0002-1223-1223

Abstract

The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

Keywords:

ECG signal, Motion Artifact, LMS algorithm, NLMS, PNLMS, IPNLMS, Wearable Devices

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Galdos, J., Lopez Colque, N., Medina Rodirguez, A., Huarca Quispe, J., Rendulich, J., & Sulla Espinoza, E. (2024). COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES. Applied Computer Science, 20(1), 157–172. https://doi.org/10.35784/acs-2024-10

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