Research On ECG Denoising And QRS Detection Algorithm Based On IEMD

For a long time,cardiovascular disease has been regarded as one of the difficulties of public medical care in the world.Cardiovascular disease has the characteristics of concealed pathological information and sudden disease occurrence.As a convenient method of screening heart conditions,electrocardiogram is inexpensive compared with other methods.,Is widely used in the preliminary examination of routine heart disease.However,due to the weak characteristics of the ECG signal collected by the hospital electrocardiograph,the signal-tonoise ratio of the signal collected by the wearable ECG acquisition device is low.Although after years of development,the ECG examination can only be used as a bridge between the patient and the clinic,and cannot be truly used.Clinical diagnosis of heart disease.For ECG signals to advance in the clinical field,further improving the accuracy of ECG signal detection,improving the signal-to-noise ratio of ECG signals and more accurate feature extraction are issues that need to be addressed.In response to the above problems,this dissertation proposes an improved IEMD method,and designs related algorithms for denoising and feature extraction of ECG signals based on the IEMD method.Theoretical analysis and experiments show that the algorithm proposed in this dissertation improves the denoising performance and QRS detection accuracy.The main research contents of this dissertation are as follows:(1)In view of the limitations of EMD,EEMD and VMD algorithms in decomposing ECG signals,this dissertation proposes the concept of amplitude scale based on further analysis of the pattern aliasing problem,and proposes a new EMD family algorithm.Interpolated Mode Decomposition(IEMD)is used to solve the problem of pattern aliasing when EMD family algorithms process non-linear,non-stationary and steep pulse signals such as ECG signals.Experiments prove that the decomposition performance of IEMD algorithm is more excellent.(2)Based on the IEMD algorithm,combined with the clinical characteristics of the ECG signal,design an ECG signal adaptive denoising algorithm.Completed the adaptive denoising of ECG signal to remove high frequency noise and baseline wandering noise.Then,the algorithm performance is verified through the ECG data in the MIT-BIH arrhythmia database,and the standard ECG signal simulated by ECGSYN is added to the simulation noise for quantitative analysis.Finally,it proves that the denoising algorithm designed in this dissertation performs more prominently.(3)Design the R wave detection algorithm combining Shannon energy and hilbert-IEMD,and use the corresponding relationship between the IMF component obtained by IEMD and the ECG signal Q and S waves to identify the Q and S points of the ECG signal,so as to carry out the heart Extraction of QRS characteristic waves of electrical signals.After processing and analyzing the MIT-BIH arrhythmia database data,this algorithm improves the accuracy of QRS wave detection and provides help for improving the efficiency of ECG clinical diagnosis.