Study On ECG Signal Quality Assessment Method

In recent years,more and more attention has been paid to the prevention and treatment of cardiovascular diseases and remote ECG monitoring.ECG signals reflect the physiological characteristics of the heart to a certain extent,and are of great clinical significance for the diagnosis,treatment and prevention of cardiovascular diseases.However,ECG signal is often corrupted with different kinds of noises and artifacts in the acquisition process,which makes the diagnosis and analysis of ECG difficult.Therefore,before clinical diagnosis and analysis by ECG physicians or automatic analysis with wearable devices,quality analysis of ECG signals is needed to filter out the unacceptable ECG signals of clinical quality.In this paper,ECG signal denoising and quality assessment are studied.In the stage of denoising,we use a method combining ensemble empirical mode decomposition(EEMD)algorithm with wavelet soft threshold.On the one hand,EEMD algorithm is used to reduce the occurrence of EMD mode aliasing.On the other hand,the wavelet soft threshold is used to reduce the loss of useful information in the process of coefficient threshold processing.In the research of signal quality assessment,ECG signal quality assessment is a subjective process.In this paper,we use the basic quality of the signal,signal quality can be judged by calculating the consistency of the QRS complex detected through the original signal and group after removing the high frequency signal.Experiments show that this method can achieve good results.At the same time,when the signal is only partially disturbed by noise,if there is enough useful information,the signal can still be used for diagnosis.This paper proposes a method of extracting quality acceptable fragments based on QRS wave group similarity,which reduces unnecessary waste of manpower and financial resources and has strong practical application value.