Background and Objectives: Wearable devices (WDs) capable of recording electrocardiograms (ECGs) for prolonged periods in ambulatory settings offer the possibility of detecting non-predictable events such as epileptic seizures and atrial fibrillation. Nevertheless, these systems suffer from additional noise sources, such as movement artifacts (MA).
Several adaptive (AF) algorithms have been proposed in the literature to suppress movement artifacts from ECG without consistent results. Adaptive algorithms for signal enhancement require a reference signal correlated with the noise and not correlated with the signal of interest. This correlation can change significantly depending on the absolute and relative location of the electrodes and the location of the reference sensor (i.e., accelerometer, gyroscope); objectively measuring the correlation is not a trivial problem.
For this reason, first, we used an algorithm to obtain a rough estimate of the movement artifacts from the recorded ECG to calculate the correlation between them and the available reference signals (three-axis accelerometer and three-axis gyroscope) and selected the one with the highest correlation. Then, we compare three adaptive filter algorithms using the signal-to-noise ratio (SNR) coefficient as the evaluation parameter.
Methods: For testing the implemented adaptive filters, first, we used a simulated signal, then data from an openonline database, and last, a single lead ECG wearable device called AFi® (Praxa Sense™, The Netherlands) with an embedded IMU. To induce the movement artifacts in a controlled setting, participants performed a set of predefined movements within three intensities; high (running, jumping), moderate (torso rotations, pushups), and low (walking). Then we analyzed the recorded data offline as follows:
1. Test the correlation between noise and the IMU components, and select the component with the highest correlation to be used as a reference input for the adaptive filters.
2. Compare three adaptive filters in terms of SNR improvement; the Least means squares (LMS), the Normalized least means squares (NLMS), and the Recursive least squares RLS.
3. Filter the selected reference input with wavelet decomposition, and test if there is a filter performance improvement in SNR.
Results: The implemented adaptive filters performed as expected with the simulated signals, but they showed very poor results once we used them on real data.
The RLS filter showed superior performance than the least mean squares-based filters in terms of convergence speed and the root mean squared error minimization. Nevertheless, it requires a high correlation (ρ) above ρ>0.8 between the reference input and the undesired signal or noise to provide a proper signal enhancement and morphology recovery.
The low correlation between the movement artifacts and the components of the IMU used as a reference input for the adaptive filters affected the filter performance heavily. Filtering the reference input with the wavelet decomposition did not improve the correlation or the filter performance.
Conclusions The correlation between ECG motion artifacts and movement recorded with inertial sensors appeared to be low and inconsistent. Given this, adaptive filters using inertial sensors as reference input are unsuitable for removing ECG movement artifacts.