An adaptive filter using electrode motion as a reference signal successfully reduced manually induced motion artifact in all tested ECG data sets.
The electrocardiogram (ECG) is the body-surface manifestation of the electrical potentials produced by the heart. The ECG is acquired by placing electrodes on the patient's skin. Motion artifact is the noise that results from motion of the electrode in relation to the patient's skin. Motion artifact can produce large amplitude signals in the ECG that may be misinterpreted by clinicians and automated systems resulting in misdiagnosis, prolonged procedure duration, and delayed or inappropriate treatment decisions. Motion artifact reduction is an unsolved problem because its frequency spectrum overlaps that of the ECG. This paper presents initial results of a novel approach to reducing ECG motion artifact. The hypothesis is that motion artifact can be reduced using electrode motion as the reference signal to an adaptive filter. Electrode motion was measured with two custom-developed sensors that utilized anisotropic magnetoresistive sensors and accelerometers. Motion artifact was induced by manually pushing on the electrode, pushing on the skin around the electrode, and pulling on the lead wire. Using an adaptive filter and the motion signal, the induced motion artifact was reduced in all data sets.
Tong et al. (Tue,) conducted a other in ECG motion artifact. Adaptive filter using electrode motion as reference signal was evaluated on Reduction of induced motion artifact. An adaptive filter using electrode motion as a reference signal successfully reduced manually induced motion artifact in all tested ECG data sets.