A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

Due to high-frequency noise and low-frequency noise in ECG signals will interfere with the accurate diagnosis of cardiovascular diseases. With the intrinsic mode function (IMF), which is the main component indicators of high-frequency noise and low-frequency noise, this paper proposes an intelligent denoising method of ECG signals based on wavelet adaptive threshold and mathematical morphology. Firstly, this method performs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signals containing noise, and adopts zero-crossing rate to identify IMFs containing high-frequency noise and low-frequency noise. Secondly, according to the discreteness and randomness of IMF containing high-frequency noise, a wavelet adaptive threshold mathematical model is constructed. In this model, with the signal-to-noise ratio (SNR) improvement as the threshold adjustment parameter, the wavelet threshold is modified by niche genetic algorithm, and the optimal solution is obtained after removing high-frequency noise by wavelet decomposition and reconstruction. The waveform of IMF containing low-frequency noise changes slowly and its amplitude is large and it is difficult to remove low-frequency noise. Therefore, mathematical morphology is used to remove low-frequency noise. Finally, the intelligent denoising method of ECG signals is designed by superimposing denoised IMFs. MIT-BIH experiments show that in the process of removing high-frequency noise and low-frequency noise, compared with other denoising methods, the percent root mean square difference (PRD) and SNR improvement of the method proposed in this paper are improved, and the denoising effect is significant, which can provide expert knowledge and decision-making guidance for related application fields.