Adaptive Electrocardiogram Enhancement in Strong Noise Environment

Qian Li1, Xingyao Wang1, Chenxi Yang1, Jianqing Li1, Chengyu Liu2
1State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China, 2Southeast University


The electrocardiogram (ECG) is a common and important indicator for diagnosing cardiovascular diseases. The wearable ECG monitoring equipment provides patients with long-term ECG monitoring. But the acquisition signals are susceptible to motion artifact (MA). Reducing MA while ECG processing will help accurately analyse the ECG and make a correct judgment on patients. This paper mainly analyses how to enhance the ECG collected under long-term monitoring and tries to propose an adaptive ECG enhancement method which is composed of adaptive division of human motion state and a modified adaptive Wiener filter based on Bayesian estimation. The method is evaluated on MITDB and CPSC2019 database, as well synchronous ECG, and three-axis acceleration data in the real world. The heart rate performance index is designed, and it is found that the heart rate calculation accuracy can be improved by 24.5% after the ECG is enhanced. It is proved that the method can achieve a good performance of ECG enhancement under different body motion states.