Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review

Overview

Journal Bioengineering (Basel)

Date 2024 Nov 27

PMID 39593769

Authors

Yifan Jia

Hongyu Pei

Jiaqi Liang

Yuheng Zhou

Yanfei Yang

Yangyang Cui

Min Xiang

Affiliations

Soon will be listed here.

Abstract

This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise. It also examines the complexity of MCG signal denoising, highlighting the challenges posed by environmental and instrumental interference. This review is the first to systematically compare the characteristics of ECG and MCG signals, emphasizing their complementary nature. MCG holds significant potential for improving the precision of CVD clinical diagnosis. Additionally, it evaluates the limitations of current denoising methods in clinical applications and outlines future directions, including the potential of explainable neural networks, multi-task neural networks, and the combination of deep learning with traditional methods to enhance denoising performance and diagnostic accuracy. In summary, while traditional filtering techniques remain relevant, hybrid strategies combining machine learning offer substantial potential for advancing signal processing and clinical diagnostics. This review contributes to the field by providing a comprehensive framework for selecting and improving denoising techniques, better facilitating signal quality enhancement and the accuracy of CVD diagnostics.

References

Meyer C, Keiser H . Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput Biomed Res. 1977; 10(5):459-70. DOI: 10.1016/0010-4809(77)90021-0. View

Singh P, Joshi S, Patney R, Saha K . The Fourier decomposition method for nonlinear and non-stationary time series analysis. Proc Math Phys Eng Sci. 2017; 473(2199):20160871. PMC: 5378250. DOI: 10.1098/rspa.2016.0871. View

Kandori A, Shimizu W, Yokokawa M, Noda T, Kamakura S, Miyatake K . Identifying patterns of spatial current dispersion that characterise and separate the Brugada syndrome and complete right-bundle branch block. Med Biol Eng Comput. 2004; 42(2):236-44. DOI: 10.1007/BF02344637. View

Chen M, Li Y, Zhang L, Liu L, Han B, Shi W . Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder. IEEE J Biomed Health Inform. 2024; 28(4):1993-2004. DOI: 10.1109/JBHI.2024.3355960. View

Sanchis J, Bodi V, Nunez J, Bosch X, Loma-Osorio P, Mainar L . Limitations of clinical history for evaluation of patients with acute chest pain, non-diagnostic electrocardiogram, and normal troponin. Am J Cardiol. 2008; 101(5):613-7. DOI: 10.1016/j.amjcard.2007.10.024. View

Samann F, Schanze T . RunDAE model: Running denoising autoencoder models for denoising ECG signals. Comput Biol Med. 2023; 166:107553. DOI: 10.1016/j.compbiomed.2023.107553. View

Hesar H, Hesar A . Adaptive augmented cubature Kalman filter/smoother for ECG denoising. Biomed Eng Lett. 2024; 14(4):689-705. PMC: 11550304. DOI: 10.1007/s13534-024-00362-7. View

Su L, Zhao G . De-Noising of ECG signal using translation- invariant wavelet de-noising method with improved thresholding. Conf Proc IEEE Eng Med Biol Soc. 2007; 2005:5946-9. DOI: 10.1109/IEMBS.2005.1615845. View

Tan C, Zhang L, Wu H . A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based Signal Compression Algorithm With Application on ECG Signals. IEEE J Biomed Health Inform. 2018; 23(2):672-682. DOI: 10.1109/JBHI.2018.2817192. View

10.

Thakor N, Zhu Y . Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng. 1991; 38(8):785-94. DOI: 10.1109/10.83591. View

11.

Hesar H, Mohebbi M . An Adaptive Particle Weighting Strategy for ECG Denoising Using Marginalized Particle Extended Kalman Filter: An Evaluation in Arrhythmia Contexts. IEEE J Biomed Health Inform. 2017; 21(6):1581-1592. DOI: 10.1109/JBHI.2017.2706298. View

12.

Takala P, Hanninen H, Montone J, Makijarvi M, Nenonen J, Oikarinen L . Magnetocardiographic and electrocardiographic exercise mapping in healthy subjects. Ann Biomed Eng. 2001; 29(6):501-9. DOI: 10.1114/1.1376388. View

13.

Martens S, Mischi M, Oei S, Bergmans J . An improved adaptive power line interference canceller for electrocardiography. IEEE Trans Biomed Eng. 2006; 53(11):2220-31. DOI: 10.1109/TBME.2006.883631. View

14.

El Bcharri O, Latif R, Elmansouri K, Abenaou A, Jenkal W . ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. Biomed Eng Online. 2017; 16(1):26. PMC: 5297224. DOI: 10.1186/s12938-017-0315-1. View

15.

Yang Y, Xu M, Liang A, Yin Y, Ma X, Gao Y . A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array. Sci Rep. 2021; 11(1):5564. PMC: 7970947. DOI: 10.1038/s41598-021-84971-7. View

16.

Fenici R, Brisinda D, Venuti A, Sorbo A . Thirty years of clinical magnetocardiography at the Catholic University of Rome: diagnostic value and new perspectives for the treatment of cardiac arrhythmias. Int J Cardiol. 2013; 168(5):5113-5. DOI: 10.1016/j.ijcard.2013.07.238. View

17.

Kwong J, Leithauser B, Park J, Yu C . Diagnostic value of magnetocardiography in coronary artery disease and cardiac arrhythmias: a review of clinical data. Int J Cardiol. 2013; 167(5):1835-42. DOI: 10.1016/j.ijcard.2012.12.056. View

18.

Milanesi M, Martini N, Vanello N, Positano V, Santarelli M, Paradiso R . Multichannel techniques for motion artifacts removal from electrocardiographic signals. Conf Proc IEEE Eng Med Biol Soc. 2007; 2006:3391-4. DOI: 10.1109/IEMBS.2006.260464. View

19.

Li P, Wang Y, He J, Wang L, Tian Y, Zhou T . High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal. IEEE Trans Biomed Eng. 2016; 64(1):78-86. DOI: 10.1109/TBME.2016.2539421. View

20.

Mian Qaisar S . Baseline wander and power-line interference elimination of ECG signals using efficient signal-piloted filtering. Healthc Technol Lett. 2020; 7(4):114-118. PMC: 7494370. DOI: 10.1049/htl.2019.0116. View