An efficient algorithm for the removal of motion artifacts in wearable ECG technology

Wearable electrocardiogram (ECG) devices are pivotal for healthcare monitoring; however, artifacts, especially from non-contact capacitive electrodes, can significantly compromise signal quality. This study presents a wavelet-based algorithm designed for the automatic removal of these artifacts from wearable ECG signals. We investigated the efficacy of various mother wavelet functions, including Haar, Daubechies, Coiflet, Symlet, Discrete Meyer, and Biorthogonal, by applying them to wearable ECG signals from the MIT-BIH Normal Sinus Rhythm Database (MIT-BIH NSRD) with artificially superimposed motion artifacts. The performance of the proposed algorithm was evaluated using several cross-correlation and mean squared error metrics to compare the cleaned signals against the raw inputs. Among the tested wavelet functions, Symlet yielded the highest cross-correlation (CC) of 0.89 and a mean squared error (MSE) of 0.0034. Further validation on the MIT-BIH Arrhythmia Database (MIT-BIH AD) and Motion Artifact-Contaminated ECG Database revealed that the proposed algorithm substantially improves artifact reduction, thereby enhancing the reliability of different wearable ECG monitoring systems.