COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES
Jarelh Galdos
National University of San Agustín of Arequipa (Peru)
https://orcid.org/0000-0001-6591-1866
Nikolai Lopez
nlopezcol@unsa.edu.peNational University of San Agustín of Arequipa (Peru)
https://orcid.org/0000-0002-5748-0717
Angie Medina
National University of San Agustín of Arequipa (Peru)
Jorge Huarca
National University of San Agustín of Arequipa (Peru)
https://orcid.org/0000-0001-6300-5122
Jorge Rendulich
National University of San Agustín of Arequipa (Peru)
https://orcid.org/0000-0003-1302-547X
Erasmo Sulla
National University of San Agustín of Arequipa (Peru)
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 DevicesReferences
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Authors
Jarelh GaldosNational University of San Agustín of Arequipa Peru
https://orcid.org/0000-0001-6591-1866
Authors
Nikolai Lopeznlopezcol@unsa.edu.pe
National University of San Agustín of Arequipa Peru
https://orcid.org/0000-0002-5748-0717
Authors
Angie MedinaNational University of San Agustín of Arequipa Peru
Authors
Jorge HuarcaNational University of San Agustín of Arequipa Peru
https://orcid.org/0000-0001-6300-5122
Authors
Jorge RendulichNational University of San Agustín of Arequipa Peru
https://orcid.org/0000-0003-1302-547X
Authors
Erasmo SullaNational University of San Agustín of Arequipa Peru
https://orcid.org/0000-0002-1223-1223
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