Motion artifact reduction algorithm using sequential adaptive noise filters and estimation methods for mobile ECG

Abstract

Electrocardiogram (ECG) is beneficial to diagnose various heart diseases. Recently, mobile ECG has received the attention of researchers and practitioners for real time monitoring and automatic diagnosis. However, the performance of ECG’s monitoring system is degraded because of many disturbances created by subject movement, which leads to wrong diagnosis. Several attempts have been performed to remove the noise from clinical ECG signal using various digital signal processing techniques. Those techniques are not directly appropriate to be used for the mobile ECG environment and expected noises. Ultimately, motion artifact still is an open issue in mobile ECG. In this paper, an algorithm is proposed to reduce motion artifact using three sequential adaptive noise filters based on least mean square errors and reference noise estimation methods. The proposed algorithm is evaluated using real dataset that was collected while the subject performing different activities. Results show promising enhancement in the ECG signal quality.