Adaptive noise cancelling of motion artifact in stress ECG signals using accelerometer

Electrocardiographic (ECG) signals obtained from stress examinations are diagnostically significant in detecting a number of heart diseases, which may not be apparent when the patient is at rest. However, the noise produced by the environment and by the patient often distorts the ECG data. Motion artifact, the most prevalent and difficult type of noise to filter in exercise ECG, corrupts the intelligibility of the desired signal thus reducing the reliability of the stress test. In this paper, the researchers aim to demonstrate a new adaptive filtering method for stress ECG signals. This noise cancellation scheme uses an accelerometer as a source of noise reference. Experiments involving single-axis and dual-axis motion sensors are conducted to evaluate the efficiency of this technique. The acquired real ECG and accelerometer data are simultaneously processed and analyzed using the two most widely used adaptive filtering algorithms, least mean squares (LMS) and recursive least squares (RLS). The results show that the proposed method can be adapted to effectively reduce motion artifact in stress ECG by just using a single-axis noise reference.