Adaptive filtering of motion artifacts in stress ECG signals using accelerometers - Raya, Mary Anne D. (2002)
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 stress test. This research aims to demonstrate a new adaptive filtering method for stress ECG signals. The noise cancellation scheme uses a uniaxial accelerometer, which is placed at the subject's lower back, 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 effect of using such number of axes on the degree of filtering of motion artifacts from stress ECG is investigated. 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 consistently show that the proposed method can be adapted to effectively reduce motion artifact in stress ECG bu just using a single-axis noise reference. In order to measure the effectiveness of the proposed method, other available methods used in stress ECG signal enhancement are employed and compared with the proposed method. These methods, which address the issue of adaptively filtering motion artifacts in exercise ECG, are the ones proposed and conducted by Tompkins (1995) and Tong (2000).
Thesis (M.S. Electrical Engineering)--University of the Philippines, Diliman.
Keywords: Adaptive signal processing; Electrocardiographs -- Noise; Stress echocardiography; Accelerometers