Valvular regurgitations in the horse: the importance of an exercise ECG

Removing movement artifacts from equine ECG recordings acquired with textile electrodes

This study reports on the implementation of a novel system to detect and reduce movement artifact (MA) contribution in electrocardiogram (ECG) recordings acquired from horses in free movement conditions. The system comprises both integrated textile electrodes for ECG acquisition and one triaxial accelerometer for movement monitoring. Here, ECG and physical activity are continuously acquired from seven horses through the wearable system and a model that integrates cardiovascular and movement information to estimate the MA contribution is implemented. Moreover, in this study we propose a new algorithm where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG recodigns. Achieved results showed a reduction of MA percentage greater than 40% between before- and after- the application of the proposed algorithm to seven hours of recordings.

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

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).