A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses

Abstract

This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p-value < 0.0.5).

Fig 1. Scheme of the electronic set up: placement of the proposed system.

Fig 2. Block diagram of the proposed method.

After a pre-processing phase, the SWT decomposition, the QRS complexes and motion detections are performed followed by the reconstruction of the signal affected by artifacts, that is used to reduce and/or remove MAs.

Fig 3. Block diagram of the QRS-complexes detection.

The method employes a threshold to detect QRS complexes based on an estimate of the energy of the second derivative of the ECG signals.

Fig 4. Example of two consecutive QRS complexes detected by the proposed method.

Letter A indicates the stating time-instant while B indicates the ending time-instant.

Fig 5. Block diagram of the Motion-detection procedure.

After the signal is rectified and subtracted by its mean value, strong movement segments are detected through a threshold as estimated in Eq (2). These motion signal segments can introduce MAs.

Fig 6. The graph at the top shows the acceleration module with an event of remarkable movement, while at the bottom the ECG with the corresponding MA is reported.

Fig 7. Histograms of the distribution of maxima (right) and minima (left) for a given decomposition level.

In red the relative thresholds are displayed.

Fig 8. Recursive scheme of an Adaptive filter.

Fig 9. Bland-Altman plot comparing QRS complex starting time instant series between the SWMAR algorithm with those provided by PhysioNet Database.

The black line indicates the bias (mean difference), the red lines are limits of agreement (mean ± 2 SD), whilst the blue line indicates the trend of the data. Mean = −0.0204 (95% CI: −0.0195 to −0.0213); limits of agreement between 0.0036 and −0.0372.

Fig 10. Bland-Altman plot comparing QRS complex ending time instant series between the SWMAR algorithm with those provided by PhysioNet Database.

The black line indicates the bias (mean difference), the red lines are limits of agreement (mean ± 2 SD), whilst the blue line indicates the trend of the data. Mean = 0.0105 (95% CI: 0.0115 to 0.0094); limits of agreement between −0.0309 and −0.0099.

Fig 11. Decomposition of a clean ECG segment.

At the top the original ECG signal is plotted, following the 5 decomposition detail signals and the approximation regarding the 5th level. Red and yellow lines represent the threshold levels as calculated according to Eqs (19) and (20).

Fig 12. Decomposition of an ECG segment affected by artifacts.

At the top the original ECG signal is plotted, following the 5 decomposition detail signals and the approximation regarding the 5th level. Red and yellow lines represent the threshold levels as calculated according to Eqs (19) and (20). The figure shows that MAs details and approximations coefficients that are out of the interval delimited by the two thresholds.

Fig 13. The upper plot reports the accelerometric signal (in red) along with the ECG signal with the corresponding artifact.

The lower plot shows the ECG signal after applying the MA removal algorithm.

Fig 14. Boxplot of the MAs percentage before and after the method of removal MAs.