Faculty of Science-Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Sensors (Basel). 2021 Dec 7;21(24):8169. doi: 10.3390/s21248169.
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.
如今，可穿戴医疗设备因其价格低廉、使用方便而受到欢迎。大多数可穿戴医疗设备允许用户连续收集和检查他们的健康数据，例如心电图（ECG）。因此，许多这样的设备已被用于监测有潜在心脏病理的患者，因为他们进行日常活动。然而，使用移动 ECG 采集心脏数据的一个主要挑战是由患者日常活动引起的基线漂移和运动伪影，导致误诊。本文提出了一种新的算法，可自动去除移动 ECG 记录中的基线漂移并抑制大多数运动伪影。与传统的噪声去除方法相比，该算法显示出了显著的改进。使用两个信号质量指标来比较参考 ECG 与其噪声版本：相关系数和均方误差。对于这两个指标，实验结果表明，经过我们的算法滤波后的噪声信号提高了十倍。
