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.
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