Abstract
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.
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References
Acharya UR, Fujita H, Oh SL et al (2018) Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl Intell 49:16–27. https://doi.org/10.1007/s10489-018-1179-1
Alyasseri ZAA, Khader AT, Al-Betar MA, Awadallah MA (2018) Hybridizing β-hill climbing with wavelet transform for denoising ECG signals. Inf Sci 429:229–246. https://doi.org/10.1016/j.ins.2017.11.026
Bari MdF, Anowarul Fattah S (2020) Epileptic seizure detection in EEG signals using normalized IMFs in CEEMDAN domain and quadratic discriminant classifier. Biomed Signal Process Control 58:101833. https://doi.org/10.1016/j.bspc.2019.101833
Bayer FM, Kozakevicius AJ, Cintra RJ (2019) An iterative wavelet threshold for signal denoising. Signal Process 162:10–20. https://doi.org/10.1016/j.sigpro.2019.04.005
Boda S, Mahadevappa M, Dutta PK (2021) A hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT. Biomed Signal Process Control 67:102466. https://doi.org/10.1016/j.bspc.2021.102466
Chen B, Yu S, Yu Y, Guo R (2019) Nonlinear active noise control system based on correlated EMD and Chebyshev filter. Mech Syst Signal Process 130:74–86. https://doi.org/10.1016/j.ymssp.2019.04.059
Chen X, Cheng Z, Wang S et al (2021) Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals. Comput Methods Programs Biomed 202:106009. https://doi.org/10.1016/j.cmpb.2021.106009
Christov I, Raikova R, Angelova S (2018) Separation of electrocardiographic from electromyographic signals using dynamic filtration. Med Eng Phys 57:1–10. https://doi.org/10.1016/j.medengphy.2018.04.007
Fujita H, Cimr D (2019) Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. Appl Intell. https://doi.org/10.1007/s10489-019-01461-0
González-Hidalgo M, Massanet S, Mir A, Ruiz-Aguilera D (2018) Improving salt and pepper noise removal using a fuzzy mathematical morphology-based filter. Appl Soft Comput 63:167–180. https://doi.org/10.1016/j.asoc.2017.11.030
Hao H, Liu M, Xiong P et al (2019) Multi-lead model-based ECG signal denoising by guided filter. Eng Appl Artif Intell 79:34–44. https://doi.org/10.1016/j.engappai.2018.12.004
Joo S, Choi J, Kim N, Lee MC (2021) Zero-crossing rate method as an efficient tool for combustion instability diagnosis. Exp Thermal Fluid Sci 123:110340. https://doi.org/10.1016/j.expthermflusci.2020.110340
Kayikcioglu İ, Akdeniz F, Köse C, Kayikcioglu T (2020) Time-frequency approach to ECG classification of myocardial infarction. Comput Electr Eng 84:106621. https://doi.org/10.1016/j.compeleceng.2020.106621
Lee M, Lee J-H (2021) A robust fusion algorithm of LBP and IMF with recursive feature elimination-based ECG processing for QRS and arrhythmia detection. Appl Intell. https://doi.org/10.1007/s10489-021-02368-5
Mukhopadhyay SK, Krishnan S (2020) A singular spectrum analysis-based model-free electrocardiogram denoising technique. Comput Methods Programs Biomed 188:105304. https://doi.org/10.1016/j.cmpb.2019.105304
Nguyen P, Kim J-M (2016) Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition. Inf Sci 373:499–511. https://doi.org/10.1016/j.ins.2016.09.033
Rakshit M, Das S (2018) An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomed Signal Process Control 40:140–148. https://doi.org/10.1016/j.bspc.2017.09.020
Sharma A, Patidar S, Upadhyay A, Rajendra Acharya U (2019) Accurate tunable-Q wavelet transform based method for QRS complex detection. Comput Electr Eng 75:101–111. https://doi.org/10.1016/j.compeleceng.2019.01.025
Sharma RR, Pachori RB (2018) Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition. Biomed Signal Process Control 45:33–49. https://doi.org/10.1016/j.bspc.2018.05.002
Singhal A, Singh P, Fatimah B, Pachori RB (2020) An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomed Signal Process Control 57:101741. https://doi.org/10.1016/j.bspc.2019.101741
Wang L, Shao Y (2020) Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis. Mech Syst Signal Process 138:106545. https://doi.org/10.1016/j.ymssp.2019.106545
Wei J, Huang H, Yao L et al (2020) New imbalanced fault diagnosis framework based on Cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data. Eng Appl Artif Intell 96:103966. https://doi.org/10.1016/j.engappai.2020.103966
Wieslander B, Xia X, Jablonowski R et al (2018) The ability of the electrocardiogram in left bundle branch block to detect myocardial scar determined by cardiovascular magnetic resonance. J Electrocardiol 51:779–786. https://doi.org/10.1016/j.jelectrocard.2018.05.019
Yao L, Pan Z (2020) A new method based CEEMDAN for removal of baseline wander and powerline interference in ECG signals. Optik 223:165566. https://doi.org/10.1016/j.ijleo.2020.165566
Yazdani S, Vesin J-M (2016) Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology. Digital Signal Processing 56:100–109. https://doi.org/10.1016/j.dsp.2016.06.010
Zhang J, Liu M, Xiong P et al (2021) A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction. Eng Appl Artif Intell 97:104092. https://doi.org/10.1016/j.engappai.2020.104092
Zhang S, Wu J, Jia Y et al (2021) A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability. Eng Appl Artif Intell 100:104206. https://doi.org/10.1016/j.engappai.2021.104206
Zhang Y, Yan B, Aasma M (2020) A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM. Expert Syst Appl 159:113609. https://doi.org/10.1016/j.eswa.2020.113609
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The project was supported by the Ministry of Education Humanities and Social Sciences Foundation of China (20YJA870006).
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Gao, L., Gan, Y. & Shi, J. A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology. Appl Intell 52, 10270–10284 (2022). https://doi.org/10.1007/s10489-022-03182-3
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DOI: https://doi.org/10.1007/s10489-022-03182-3