Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

Fuad A Ghaleb, Maznah Bte Kamat, Mazleena Salleh, Mohd Foad Rohani, Shukor Abd Razak, Fuad A Ghaleb, Maznah Bte Kamat, Mazleena Salleh, Mohd Foad Rohani, Shukor Abd Razak

Abstract

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Adaptive noise filter concept.
Fig 1. Adaptive noise filter concept.
Fig 2. Block diagram of the proposed…
Fig 2. Block diagram of the proposed motion artefact reduction algorithm.
Fig 3. Hampel filter based estimation.
Fig 3. Hampel filter based estimation.
Fig 4. Hampel filter-based estimation.
Fig 4. Hampel filter-based estimation.
Fig 5. Conceptual structure of hampel filter-based…
Fig 5. Conceptual structure of hampel filter-based filtering.
Fig 6. The correlation between the acceleration…
Fig 6. The correlation between the acceleration signals and the ECG signal at different lags.
Fig 7. The correlation between the ECG…
Fig 7. The correlation between the ECG signal and the acceleration signal.
Fig 8. Filtering results using the ECG…
Fig 8. Filtering results using the ECG signal recorded during walking.
Fig 9. Filtering results using the ECG…
Fig 9. Filtering results using the ECG signal recorded during running.
Fig 10. Filtering results using the ECG…
Fig 10. Filtering results using the ECG signal recorded during free movement.
Fig 11. Filtering results using the ECG…
Fig 11. Filtering results using the ECG signal recorded during knee bending exercise.
Fig 12. Example of treatment of heavey…
Fig 12. Example of treatment of heavey contaminated ECG signal.
Fig 13. Filtering results using the ECG…
Fig 13. Filtering results using the ECG signal recorded during walking following by jogging exercise.
Fig 14. Summary of the performance during…
Fig 14. Summary of the performance during different forms of exercise.
Fig 15. The QRS complex detection accuracy…
Fig 15. The QRS complex detection accuracy before and after the filtering using the proposed methods.

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