Abstract
Today’s wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient’s daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.
Highlights
Faculty of Science—Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada; Abstract: Today’s wearable medical devices are becoming popular because of their price and ease of use
This paper proposes a new noise removal method that automatically suppresses the baseline wander and motion artifact
The filter step consists of an adaptive empirical mode decomposition and reconstruction step that could automatically decompose the signal without human input (Section 2.2.4) to reduce motion wander, a motion-sensitive adaptive filter (Section 2.3) that uses a 3-axis accelerometer that automatically selects the best reference noise signal to remove motion artifacts, and a variational mode decomposition and reconstruction method (Section 2.3.1) to remove high-frequency noise
Summary
Electrocardiogram (ECG) is one of the most used medical tools for cardiologists to detect heart anomalies. Today’s rapid development of cloud technology allows new medical products such as QardioMD [1], Vivalink [2], and Astroskin Smart Shirt [3] to measure numerous physiological indicators simultaneously and transmit this information to a remote database in the so-called cloud Using this mobile technology, one can evaluate a patient’s condition as they perform their daily activities. The commercial personal medical device possesses many advantages such as ease of use, long-term body monitoring, and easy access to personal health data. Two major noise types to the ECG signal: baseline wander (Figure 1) and motion artifact (Figure 2).
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