Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review
Abstract
1. Introduction
2. Relationship Between MCG and ECG Signal
3. Noises in ECG Signal
4. Pre-Processing in ECG Signal
4.1. BW Removal in ECG Signal
4.2. EMG/MA Removal in ECG Signal
4.3. PLI Removal in ECG Signal
4.4. Composite Noise Removal in ECG
Methods | Database | Record | Evaluation Parameters |
---|---|---|---|
Schrodinger equation followed by a new wavelet [38] | MIT-BIH and experimental data | 105, 234 | = 20 dB (avg.) MSE = 0.014 (avg.) PRD = 0.014 (avg.) |
Variable frequency complex demodulation (VFCDM) algorithm [59] | MIT-BIH and Wearable Armnand ECG Data and NSTDB | 100, 101, 103, 105, 106, 115, 215 and 230 | = 7.5015 dB, PRD = 7.44%, WEDD = 6.52% (for 15 dB I/P SNR) |
Variable-frequency complex demodulation (VFCDM) algorithm [134] | Experimental data | - | Emax = 19.01%, Eavg = 7.88%, WEDD = 4.70% |
Singular spectrum analysis and digital filtering [22] | MIT-BIH, NSTDB, PTBDB | - | = 41.96 dB, PRD = 5.71%, WEDD = 2.99% (for MIT-BIH) |
Periodic non-local means filter [92] | MIT-BIH, Real noisy ECG signals | 103, 105, 106, 109, 115, 121, 201, 203, 209, 231 and 233 | = 8.855 dB, MSE = 0.0010, PRD > 10% (for 10 dB I/P SNR) |
Bandpass filter and group sparsity and singular spectrum analysis [154] | Simulate ECG data | s20011, s20031, s20041, s20051, s20061, s20071, s20081, s20091 and s20101 | = 10 dB (for 10 dB I/P SNR) |
Adaptive dual-threshold filter and discrete wavelet transform [104] | MIT-BIH | 100, 101, 103, 105, 115, 122, 124 and 231 | = 7.143 dB, RMSE = 0.0042 (for 5 dB I/P SNR) |
Discrete wavelet transform and non-local means (NLM) estimation [143] | MIT-BIH | 100, 103, 105, 106, 115, 215 | = 8.064 dB (for 10 dB I/P SNR), MSE = 0.0037, PRD = 5.5% |
Dual-tree wavelet transform [71] | Simulated ECG data | 203, 109, 119, 111 and 108 | SNR = 30.493 dB (for 5 dB I/P SNR for record 108) |
Biorthogonal wavelet transform and adaptive slope prediction-based threshold [72] | MIT-BIH, PTB, ST | 80 + 365 records | SNR = 32.6568 dB, MSE = 0.0003 |
Biorthogonal wavelet transform and wavelet-Wiener filter thresholding [139] | QT database | 46 records | Avg. SNR = 4.2568 dB, MSE = 0.008, Avg. PRD = 12.14% (10 dB) Avg. SNR = 4.5594 dB, Avg. MSE = 0.0023, Avg. PRD = 11.19% (5 dB) |
Local means (LM) method [137] | MIT-BIH, ST | 100, 103, 104, 105, 106, 115, 215 | Avg. = 10 dB, Avg. MSE = 0.0003, Avg. PRD < 12% (10 dB I/P SNR) Avg. = 10 dB, Avg. MSE = 0.0003, Avg. PRD ≈ 15% (for 10 dB I/P SNR) |
Riemann–Liouville fractional integral filtering and Savitzky–Golay filtering and empirical mode decomposition [76] | MIT-BIH | 115 | SNR = 7.5288 dB, MSE = 0.0027 |
EMD and non-local means [149] | MIT-BIH | 215, 115, 106, 105, 104, 103, 100 | Avg. > 8 dB, Avg. MSE = 0.000472, Avg. PRD < 20% (for 10 dB I/P SNR) |
EMD and adaptive switching mean filter (ASMF) [98] | MIT-BIH | 100, 101, 103, 105, 115, 200, 215, and 230 | > 8 dB, MSE ≈ 0.002, PRD < 15% (for 10 dB I/P SNR) |
Eigenvalue decomposition of the Hankel matrix [77] | MIT-BIH | 100, 101, 103, 105, 108, 109, 111, 112, 113, 115, 116, 117, 118, 121, 122, 123, 210, and 212 | = 12.89 dB, PRD ≈ 20% (for 5 dB I/P SNR and record 100) |
Methods | Database | Record | Evaluation Parameters |
---|---|---|---|
Dual-tree complex wavelet transform and non-negative garrotte threshold function [141] | MIT-BIH | all 48 records | SNR = 58.23 dB, MSE = 0.0000000963, PRD = 0.001 (for record 100) |
Cooperative filtering of similar segments and Savitzky–Golay and polynomial fitting [138] | MIT-BIH | 100, 101, 103, 104, 105, 106, 113, 115, 200, 215, 230 and 231 | ≥ 8 dB, MSE = 0.0010, PRD = 23.87% (for 5 dB I/P SNR and record 100) |
Running denoising autoencoder [78] | Simulated ECG signals | - | ≈ 20 dB, MSE < 0.00005 (for 5 dB I/P SNR) |
Non-local wavelet transform domain filtering [142] | MIT-BIH, PTB | 100, 103, 104, 105, 106, 115 and 215, s0032, s0207, s0508, s0510, s0430, s0035, s0354, s0370, s0003, s0012, s0432, s0390 | ≈ 6 dB, PRD ≈ 12%, MSE < 0.003 (for 20 dB I/P SNR and record 103), = 19.18 dB, PRD = 19.1%, MSE = 0.001 (for record s0032) |
DAE using the fully convolutional network (FCN) [158] | MIT-BIH | all 48 records | Avg. > 8 dB, Avg. RMSE = 0.063, Avg.PRD = 19.68% (for −1 dB I/P SNR) |
Stationary wavelet total variation algorithm [102] | MIT-BIT | 100, 103, 105, 113, 115, 117, 119, 122, 200, 215, 213, 230, 231 and 234 | SNR = 25.44 dB, RMSE = 0.3940, PRD = 40% (for 5 dB I/P SNR at record 231) |
Fractional Stockwell transform (FrST) [79] | MIT-BIT, ST | 100, 101, 102, 103, 113, 201, 207, 217, 231, e0103, e0104, e0105 and e0106 | = 19.0699 dB, RMSE = 0.0807, PRD ≈ 8% (for 15 dB I/P SNR and record 100), = 24.6292 dB, RMSE = 0.0469 (for 15 dB I/P SNR and record e0103) |
Ensemble empirical mode decomposition and genetic-algorithm-based thresholding technique [148] | MIT-BIH | - | > 5 dB, MSE < 0.05, PRD < 20% (for 10 dB I/P SNR) |
Real-time accurate thresholding method and discrete wavelet transform [58] | MIT-BIH | 233 | = 6.0286 dB, RMSE = 0.2705 |
Particle swarm optimization and wavelet transform [117] | MIT-BIH | 100 | SNR = 14.5189 dB, MSE = 0.143, RMSE = 0.1103, PRD = 20.1725% (for 5 dB I/P SNR) |
U-Net [73] | PTB-XL, CPSC2018 | Manually label | SNR = 20.60 dB, RMSE = 0.0111 (for 20 dB I/P SNR) |
Transformer encoder [163] | QT database | - | SNR = 13.60 dB, RMSE = 0.06, PRD = 22.85% (for 0–6 dB I/P SNR) |
Sparse coding and Kalman filter [145] | QT database | - | = 18.6 dB, MAE = 0.026, (for −5 dB I/P SNR) |
Bidirectional gated recurrent units [74] | Experimental data collected using wearable sensors | - | = 18.9 dB, RMSE = 0.029, PRD = 6.4% |
Transformer and convolutional network [160] | MIT-BIH | all 48 records | = 8.49 dB, MSE = 0.035, (for 0 dB I/P SNR) |
5. Noises in MCG Signal
6. Pre-Processing in MCG Signal
Methods | MCG Acquiring Device | Noise Source | Evaluation Parameters |
---|---|---|---|
Variational mode decomposition (VMD) and whale optimization algorithm (WOA) [178] | Simulated MCG signal and actual MCG signals collected by SQUID gradiometers | Baseline drift noise, industrial frequency noise, and Gaussian white noise | = 11.7892 dB, PRD = 0.5841, CC = 0.7218 (for 0 dB I/P SNR) |
Independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) [191] | Actual MCG signals collected by a 37- channel SQUID | High-frequency baseline drifts, low-frequency baseline drift, breathing artifact, 50 Hz PLI, high-frequency random noise, etc. | ≈ 11.7892 dB |
Convolutional neural network (CNN) [181] | Simulated MCG signal | noise | RMSE ≈ 0.03 (avg.) |
Fourier wavelet denoising and sparse representation [180] | Simulated noise | High-frequency noise, power frequency noise, and low-frequency noise | SNR = 10.5521 dB, MSE = 0.0199 (for high-frequency noise) SNR = 17.5935 dB, MSE = 0.0035 (for power frequency noise) SNR = 10.4488 dB, MSE = 0.0232 (for low-frequency noise) |
Correlation-based beat-by-beat approach and principal component analysis (PCA) [25] | Actual MCG signals collected by a 37- channel SQUID | Subjects with implanted devices | = 30 dB |
Signal space separation (SSS) and projection operation [179] | Actual MCG signals collected by 48-channel tunneling magnetoresistance (TMR) sensors | Environmental magnetic sensor noise | Reduces the environmental magnetic noise by −73 dB and the sensor noise by about −23 dB |
Independent component analysis (ICA) and EMD [26] | Actual MCG signals collected by an SERF atomic magnetometer array | Environmental noise, baseline drift, respiratory interference, and power line noise | Obvious characteristics of P wave, QRS wave, and T wave |
7. Performance Evaluation
8. Discussion and Challenges
9. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AWGN | Additive White Gaussian Noise: A type of noise with a constant intensity across all frequencies. |
CC | Correlation Coefficient: A measure of how strongly two variables are related. |
CNN | Convolutional Neural Network: A type of deep learning model often used for image and signal processing. |
CVD | Cardiovascular Disease: Diseases that affect the heart and blood vessels. |
DAEs | Denoising Autoencoders: Neural networks used to remove noise from signals or images. |
ECG | Electrocardiography: A test that measures the electrical activity of the heart. |
EKF | Extended Kalman Filter: A method used to estimate the state of a system when there is noise. |
EMs | Electrode Motion Artifacts: Noise caused by electrode movement during measurements. |
EMD | Empirical Mode Decomposition: A technique used to break down signals into simpler components. |
EMG | Electromyogram: A test that measures the electrical activity in muscles. |
GAN | Generative Adversarial Network: A model where two neural networks work together to generate realistic data. |
ICA | Independent Component Analysis: A method used to separate mixed signals into independent sources. |
IMF | Intrinsic Mode Function: Basic components of a signal derived through EMD. |
MFMs | Magnetic Field Maps: Visual representations of magnetic fields. |
MCG | Magnetocardiography: A test that records the magnetic fields generated by the heart. |
MAs | Muscle Artifacts: Noise from muscle activity that interferes with signal measurements. |
MSE | Mean Squared Error: A metric that measures the average squared difference between actual and predicted values. |
NLM | Non-Local Means: A method for reducing noise by averaging similar data points. |
OPM | Optically Pumped Magnetometer: A device used to measure small magnetic fields. |
PCD | Pseudocurrent Density: A representation of current flow patterns based on magnetic data. |
PCA | Principal Component Analysis: A technique used to reduce the dimensions of data by finding important patterns. |
PLI | Power Line Interference: Noise from electrical power systems in signal recordings. |
PRD | Percentage Root-Mean-Square Difference: A measure of the difference between two signals. |
RSM | Residual Signal Method: A way to isolate and analyze noise in signals. |
SNR | Signal-to-Noise Ratio: A measure of signal strength compared to noise. |
SQUID | Superconducting Quantum Interference Device: A highly sensitive tool for measuring magnetic fields. |
SSA | Singular Spectrum Analysis: A method for breaking down time series data into simpler parts. |
SSS | Signal Space Separation: A method for separating signal from noise in magnetoencephalography and magnetocardiography. |
SWT | Stationary Wavelet Transform: A method for analyzing signals without losing time information. |
VMD | Variational Mode Decomposition: A method for separating signals into different modes for analysis. |
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Methods | Database | Record | Evaluation Parameters |
---|---|---|---|
Variable-frequency complex demodulation (VFCDM) algorithm [59] | MIT-BIH and wearable armband ECG data and NSTDB | 100, 101, 103, 105, 106, 115, 215, 230 | = 4.17 dB, PRD = 5.79%, WEDD = 4.82% (for 20 dB I/P SNR) |
Singular spectrum analysis and digital filtering [22] | MIT-BIH | 115 | = 29.19 dB, MSE = 0.000307 |
Fourier decomposition method (FDM) [40] | MIT-BIH | 100, 101, 103, 105, 109, 111, 112, 113, 115, 116, 117, 118, 122, 123, 210, 212 | = 23.0 dB, PRD > 5% (for 5 dB I/P SNR and record 100) |
Denoising autoencoder (DAE) [45] | 1st China Physiological Signal Challenge 2018 (CPSC) | - | = 13.50 dB (for ) = 20.54 dB (for ) = 23.78 dB (for ) = 25.92 dB (for ) |
Linear time-invariant filtering and sparse optimization [51] | MIT-BIH | 103, 105, 213 | = 17.87 dB, MSE = 0.003 (for 5 dB I/P SNR and record 103) |
Recursive filtering [75] | Long-term ST | s20011, s20021, s20031, s20041, s20051, s20061, s20071, s20081, s20091 and s20101 | SNR = 19.12 dB (s20011) |
Dual-tree wavelet transform [71] | Simulated ECG data | - | = 15.24564 dB, MSE = 0.00044 (for 5 dB I/P SNR) |
Biorthogonal wavelet transform and Adaptive slope prediction-based threshold [72] | MIT-BIH, BIDMC, PTB, ST | 16 records 15 records 80 + 365 records 17 records | SNR = 28.3821 dB, MSE = 0.0029 |
Real-time accurate thresholding method and discrete wavelet transform [58] | MIT-BIH | 233 | = 3.03255 dB, RMSE = 0.24123 |
Recursive filtering [49] | Long-term ST | s20011, s20051, s20061, s20071, s20081 and s20102 | SNR = 14.39 dB (s20012) |
Riemann–Liouville fractional integral filtering and Savitzky–Golay (SG) filtering and EMD [76] | MIT-BIH | 115 | SNR = 7.6487 dB, MSE = 0.0026 |
Eigenvalue decomposition of the Hankel matrix [77] | MIT-BIH | 100, 101, 103, 105, 108, 109,111, 112, 113, 115, 116, 117, 118, 121, 122, 123, 210, 211, 212 | = 8.39 dB, PRD ≈ 30% (for 5 dB I/P SNR and record 100) |
Running denoising autoencoder [78] | Simulated ECG signals | - | ≈ 20 dB, MSE < 0.000005 (for 5 dB I/P SNR) |
DNN based on the improved DAE and wavelet transform (WT) [70] | MIT-BIH | 103, 105, 111, 116, 122, 205, 213, 219, 223 and 230 | SNR = 23.89 dB, RMSE = 0.025 (for 5 dB I/P SNR and record 103) |
Fractional Stockwell transform (FrST) [79] | MIT-BIT | 100 and 222 | = 25.0793 dB, RMSE = 0.0397 (for record 100) |
Methods | Database | Record | Evaluation Parameters |
---|---|---|---|
Deep neural network [80] | MIT-BIT | 103, 105, 111, 116, 122, 205, 213, 219, 223 and 230 | SNR = 20.636 dB, RMSE = 0.0446 (for 1.25 dB I/P SNR and avg.) |
CNN and discrete wavelet transform [81] | MIT-BIH | 103, 105, 111, 116, 122, 205, 213, 219, 223, 230 | SNR = 7.713 dB, RMSE = 0.294 (for 0 dB I/P SNR and avg.) |
CNN and stationary wavelet transform and transformer encoder [82] | MIT-BIH | 103, 105, 111, 116, 122, 205, 213, 219, 223, 230 | SNR = 29.07 dB, RMSE = 0.017 (for 0 dB I/P SNR) |
U-Net [73] | PTB-XL, CPSC2018 | Manually label | SNR = 19.51 dB, RMSE = 0.0132 (for 0 dB I/P SNR) |
Mamba [83] | QT database | - | MSE = 0.3445, PRD = 36.861% |
Bidirectional gated recurrent units [74] | Experimental data collected using wearable sensors | - | = 20.6 dB, RMSE = 0.024, PRD = 5.5% |
Methods | Database | Record | Evaluation Parameters |
---|---|---|---|
Successive local filtering algorithm [97] | MIT-BIH | 101, 103, 113, 115, 203, 207, 208, 213 | > 8 dB |
Variable-frequency complex demodulation algorithm [59] | MIT-BIH and NSTDB | 100, 101, 103, 105, 106, 115, 215, 230, Self-acquired data | = 4.17 dB, PRD = 5.79% |
Stationary wavelet total variation algorithm [102] | MIT-BIH | 100, 103, 105, 113, 115, 117, 119, 122, 200, 215, 213, 230, 231, 234 | SNR = 25.44 dB, RMSE = 0.3940, PRD = 40% |
Periodic non-local means filter [92] | MIT-BIH | 100, 103, 104, 105, 106, 115, 215 | = 5.804 dB, MSE = 0.0020, PRD > 15% (for 10 dB I/P SNR) |
Fractional filtering and zero-phase filtering and parallel-type filter [103] | MIT-BIH | 115 | SNR = 13.6817 dB, MSE = 0.0146 |
Adaptive dual-threshold filter and discrete wavelet transform [104] | MIT-BIH | 115 | MSE = 0.0069 |
Discrete wavelet transform and electrophysiological morphology [90] | MIT-BIH | All 48 records | = 7.55 dB (for 10 dB I/P SNR) |
Riemann–Liouville fractional integral filtering and Savitzky–Golay filtering and EMD [76] | MIT-BIH | 115 | SNR = 10.6116 dB, MSE = 0.0013 |
EMD and adaptive switching mean filter empirical [98] | MIT-BIH | 100, 101, 103, 105, 115, 200, 215, 230 | = 8.7879 dB, MSE = 0.00232, PRD = 11.5257% (for 10 dB I/P SNR) |
Real-time accurate thresholding method and discrete wavelet transform [58] | MIT-BIH | 233 | = 1.16789 dB, RMSE = 0.12233 |
CNN and discrete wavelet transform [81] | MIT-BIH | 103, 105, 111, 116, 122, 205, 213, 219, 223, 230 | SNR = 7.738 dB, RMSE = 0.292 (for 0 dB I/P SNR and avg.) |
CNN and stationary wavelet transform and transformer encoder [82] | MIT-BIH | 103, 105, 111, 116, 122, 205, 213, 219, 223, 230 | SNR = 28.11 dB, RMSE = 0.021 (for 0 dB I/P SNR) |
U-Net [73] | PTB-XL, CPSC2018 | Manually label | SNR = 16.76 dB, RMSE = 0.0182 (for 0 dB I/P SNR) |
Bidirectional gated recurrent units [74] | Experimental data collected using wearable sensors | - | = 19.2 dB, RMSE = 0.029, PRD = 6.4% |
Methods | Database | Record | Evaluation Parameters |
---|---|---|---|
Variable-frequency complex demodulation (VFCDM) algorithm [59] | MIT-BIH and wearable armband ECG data and NSTDB | 100, 101, 103, 105, 106, 115, 215, 230 | = 4.17 dB, PRD = 5.79%, WEDD = 4.82% (for 20 dB I/P SNR) |
Singular spectrum analysis [134] | Simulated data | - | Eave = 0.004 |
Singular spectrum analysis and digital filtering [22] | MIT-BIH | 115 | Eave = 31.9, MSE = 0.000007 |
Stationary wavelet transform (SWT) [6] | MIT-BIH | 100, 101, 102, 103, 104, 105, 109, 112, 117, 118, 123, 200, 205, 213, 221, 231, and 234 | = 49.35 dB, RMSE = 0.0006, PRD = 0.254 (for record 100 at 14.32 dB I/P SNR) |
Fourier decomposition method (FDM) [40] | MIT-BIH | 100, 101, 103, 105, 109, 111, 112, 113, 115, 116, 117, 118, 122, 123, 210, and 212 | = 28.1 dB, PRD > 0% (for 5 dB I/P SNR and record 100) |
Fractional filtering and zero-phase filtering and parallel-type filter [103] | MIT-BIH | 115 | SNR = 14.2565 dB, MSE = 0.0128 |
Adaptive dual-threshold filter and discrete wavelet transform [104] | MIT-BIH | 115 | MSE = 0.0015 |
Biorthogonal wavelet transform and adaptive slope prediction-based threshold [72] | BMDMC | 16 records | SNR = 30.0051 dB, MSE = 0.0008 |
Riemann–Liouville fractional integral filtering and Savitzky–Golay (SG) filtering and EMD [76] | PTB | 80 + 365 records | SNR = 12.0526 dB, MSE = 0.0096 |
EMD and adaptive switching mean filter (ASMF) [98] | MIT-BIH | 100, 101, 103, 105, 115, 200, 215, and 230 | > 10 dB, MSE ≈ 0.002, PRD < 10% (for 10 dB I/P SNR) |
Eigenvalue decomposition of the Hankel matrix [77] | MIT-BIH | 100, 101, 102, 103, 104, 105, 109, 112, 117, 118, 123, 200, 205, 213, 221, 231, and 234 | SNR = 49.35 dB, RMSE = 0.0006, PRD = 0.254 (for record 100 at 14.32 dB I/P SNR) |
Particle swarm optimization and wavelet transform [117] | MIT-BIH | 100, 102, 103, 105, 109 | SNR = 19.74 dB, MSE = 0.0014, RMSE = 0.0373, PRD = 10.33% (for record 100 at 5 dB I/P SNR) |
Variational mode decomposition and notch filter [129] | MIT-BIH | 100, 101, 103, 105, 108, 109, 111, 112, 113, 115, 116, 117, 118, 121, 122, 123, 210, and 212 | = 21.09 dB, CC = 0.9898 (for 5 dB I/P SNR and record 100) |
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Jia, Y.; Pei, H.; Liang, J.; Zhou, Y.; Yang, Y.; Cui, Y.; Xiang, M. Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review. Bioengineering 2024, 11, 1109. https://doi.org/10.3390/bioengineering11111109
Jia Y, Pei H, Liang J, Zhou Y, Yang Y, Cui Y, Xiang M. Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review. Bioengineering. 2024; 11(11):1109. https://doi.org/10.3390/bioengineering11111109
Chicago/Turabian StyleJia, Yifan, Hongyu Pei, Jiaqi Liang, Yuheng Zhou, Yanfei Yang, Yangyang Cui, and Min Xiang. 2024. "Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review" Bioengineering 11, no. 11: 1109. https://doi.org/10.3390/bioengineering11111109
APA StyleJia, Y., Pei, H., Liang, J., Zhou, Y., Yang, Y., Cui, Y., & Xiang, M. (2024). Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review. Bioengineering, 11(11), 1109. https://doi.org/10.3390/bioengineering11111109