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Adaptive Fourier Decomposition Approach to ECG denoising Presented by Wang, Ze (D-B0-2906-8) Supervisor: Dr. Wan, Feng Department of Electrical and Electronics.

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Presentation on theme: "Adaptive Fourier Decomposition Approach to ECG denoising Presented by Wang, Ze (D-B0-2906-8) Supervisor: Dr. Wan, Feng Department of Electrical and Electronics."— Presentation transcript:

1 Adaptive Fourier Decomposition Approach to ECG denoising Presented by Wang, Ze (D-B ) Supervisor: Dr. Wan, Feng Department of Electrical and Electronics Engineering Faculty of Science and Technology 10/6/2014 1

2 Outline 2  Introduction  Adaptive Fourier Decomposition (AFD)  Contributions  Denoising Method Based on the AFD  Denoising Technique  Judgment – Energy Ratio  Implementation  Simulation Results  Conclusion and Future Work

3 Introduction 3 Good Properties

4 Adaptive Fourier Decomposition 4 Mathematical Foundation: Basis function: Mono-components: positive phase derivatives Takenaka-Malmquist system

5 5 Mathematical Foundation: Recursive Process Adaptive Fourier Decomposition

6 6 Example: Adaptive Fourier Decomposition

7 Blue: N-th mono-component Red: Combination of first N mono-components N=2 7 N=5 N=3N=4 N=6 N= %92.22%95.66% 99.91%99.73%99.00% Adaptive Fourier Decomposition

8 8  Properties:  Different decomposition levels  Decomposition level N  Converge fast Different energy Energy of mono-components Adaptive Fourier Decomposition

9 9  AFD-based denoising method  Judgment based on the estimated SNR  Simulations  ECG signals  An artificial ECG signal  Real ECG signals  Noise  Additive Gaussian white noise  Muscle and electrode motion Artifacts  Comparison  Butterworth low-pass filter  Wavelet transform  Empirical mode decomposition (EMD)  Ensemble empirical mode decomposition (EEMD) Contributions

10 Denoising Method Based on the AFD 10

11  Assumption:  Technique: First several mono-components Original signal Denoising Technique of the AFD 11 Noisy artificial signal

12 12 Red: original signal Blue: reconstructed signal Combine First 2 components Denoising Technique of the AFD

13 13 Red: original signal Blue: reconstructed signal Combine First 6 components Denoising Technique of the AFD

14 14 Red: original signal Blue: reconstructed signal Combine First 10 components Denoising Technique of the AFD

15 15 Red: original signal Blue: reconstructed signal Combine First 18 components Denoising Technique of the AFD

16 16 Red: original signal Blue: reconstructed signal Combine First 40 components Redundancy Denoising Technique of the AFD

17 17 Red: original signal Blue: reconstructed signal Combine First 60 components Redundancy Denoising Technique of the AFD

18 18 Red: original signal Blue: reconstructed signal Combine First 80 components Redundancy Denoising Technique of the AFD

19 Judgment – Energy Ratio 19  Threshold of the decomposition level = Difficulty  New judgment:  Threshold of the energy ratio: SNR e : estimated SNR of the noisy signal

20 20 Energy ratio Threshold Relationship Judgment – Energy Ratio

21 21 Energy ratio Threshold Relationship Judgment – Energy Ratio

22 22 Implementation Threshold  Denoising Steps: 1.SNR e → Threshold

23 23 Threshold  Denoising Steps: 1.SNR e → Threshold 2.Energy Ratio Red: original signal Blue: filtered signal Implementation

24 24 Threshold  Denoising Steps: 1.SNR e → Threshold 2.Energy Ratio Red: original signal Blue: filtered signal Implementation

25 25 Threshold  Denoising Steps: 1.SNR e → Threshold 2.Energy Ratio 3.Once  Stop AFD  Reconstruct signal Red: original signal Blue: filtered signal Implementation

26 26  Denoising Steps: 1.SNR e → Threshold 2.Energy Ratio 3.Once  Continue → Redundancy Threshold  Stop AFD  Reconstruct signal Redundancy Red: original signal Blue: filtered signal Implementation

27 27 Start N=1 Decompose N-th mono-component ? N=N+1 Finish Reconstruct the original signal by using first N mono-components No Yes Old judgment: decomposition level Implementation

28 28 Start N=1 Decompose N-th mono-component ? N=N+1 Finish Reconstruct the original signal by using first N mono-components No Yes New judgment: energy ratio Implementation

29 Simulation Results 29

30 Simulation: real ECG signals + additive Gaussian white noise 30 Real ECG signals from MIT-BIH Arrhythmia Database Additive Gaussian white noise

31 31 Denoising  AFD  Wavelet transform  EMD  EEMD Simulation: real ECG signals + additive Gaussian white noise

32 32 SNR of noisy signals (dB) SNR of filtered results (dB) Wavelet transform with DB4 Wavelet transform with DB6 AFD Wavelet transform results: Ercelebi, E., “Electrocardiogram signals denoising using lifting-based discrete wavelet transform”. Computers in Biology and Medicine, Vol. 34, No. 6, pp. 479–493. Simulation: real ECG signals + additive Gaussian white noise

33 33 Record No. MSE of filtered results EMDEEMDAFD EMD and EEMD results: Chang, K. M. and Liu, S. H., “Gaussian noise filtering from ECG by wiener filter and ensemble empirical mode decomposition”. Journal of Signal Processing Systems, Vol. 64, No. 2, pp. 249–264. SNR of noisy signals: 10dB.

34 34 Real ECG signals from the MIT-BIH Arrhythmia Database Electrode motion artifact from the MIT-BIH Noise Stress Database Muscle artifact from the MIT-BIH Noise Stress Database Simulation: real ECG signals + muscle and electrode motion artifacts

35 35 Denoising  AFD  Butterworth low-pass filter  EMD  Wavelet transform Simulation: real ECG signals + muscle and electrode motion artifacts

36 36 Record No. SNR of noisy signals = 6dB SNR of noisy signals = 10dB SNR of noisy signals = 14dB SNR emd SNR butt SNR wt SNR AFD SNR emd SNR butt SNR wt SNR AFD SNR emd SNR butt SNR wt SNR AFD The EMD, Butterworth low-pass filter, wavelet transform results: Blanco-Velasco, M., Weng, B. and Barner, K. E., “ECG signal denoising and baseline wander correction based on the empirical mode decomposition”. Computers in Biology and Medicine, Vol. 38, No. 1, pp. 1–13. Simulation: real ECG signals + muscle and electrode motion artifacts

37 Conclusion 37  AFD-based denoising method  Judgment: energy ratio  Simulations  ECG signals  An artificial ECG signal  Real ECG signals  Noise  Additive Gaussian white noise  Muscle and electrode motion Artifacts  Comparison  Butterworth low-pass filter  Wavelet transform  Empirical mode decomposition (EMD)  Ensemble empirical mode decomposition (EEMD) AFD Promising Tool for ECG denoising

38 38  Other applications of the AFD  Converge fast → Signal and image compression  Mono-components → Non-negative phase derivatives → Instantaneous frequency Future Work

39 39 [1]Blanco-Velasco, M., Weng, B. and Barner, K. E., “ECG signal denoising and baseline wander correction based on the empirical mode decomposition”. Computers in Biology and Medicine, Vol. 38, No. 1, pp. 1–13. [2]Chang, K. M. and Liu, S. H., “Gaussian noise filtering from ECG by wiener filter and ensemble empirical mode decomposition”. Journal of Signal Processing Systems, Vol. 64, No. 2, pp. 249–264. [3]Ercelebi, E., “Electrocardiogram signals denoising using lifting-based discrete wavelet transform”. Computers in Biology and Medicine, Vol. 34, No. 6, pp. 479–493. [4]Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C. K. and Stanley, H. E., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals”. Circulation, Vol. 101, No. 23, pp. e215–e220. [5]McSharry, P. E., Clifford, G. D., Tarassenko, L. and Smith, L. A., “Adynamical model for generating synthetic electrocardiogram signals”. IEEE Transactions on Biomedical Engineering, Vol. 50, No. 3, pp. 289–294. [6]Moody, G. B. and Mark, R. G., “The impact of the MIT-BIH Arrhythmia Database”. IEEE Engineering in Medicine and Biology Magazine, Vol. 20, No. 3, pp. 45–50. [7]Moody, G. B., Muldrow, W. and Mark, R. G., “A noise stress test for arrhythmia detectors”. Computers in Cardiology, Vol. 11, No. 3, pp [8]Qian, T., Wang, Y. B. and Dang, P., “Adaptive decomposition into mono-components”. Advances in Adaptive Data Analysis, Vol. 1, No. 4, pp. 703–709. [9]Qian, T., Zhang, L. and Li, Z., “Algorithm of adaptive Fourier decomposition”. IEEE Transactions on Signal Processing, Vol. 59, No. 12, pp. 5899–5906. References

40 40 1)Ze Wang, Chi Man Wong, Janir Nuno da Cruz, Feng Wan, Pui-In Mak, Peng Un Mak and Mang I Vai, “Muscle and electrode motion artifacts reduction in ECG using adaptive Fourier decomposition”, the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2014). Under review. 1)Wei Chen, Ze Wang, Ka Fai Lao and Feng Wan, “Ocular artifact removal from EEG Using ANFIS”, the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014). Accepted. 1)Ze Wang, Chi Man Wong, Janir Nuno da Cruz, Feng Wan, Pui-In Mak, Peng Un Mak and Mang I Vai, “Adaptive Fourier decompostion approch for ECG denosing”, Electronics Letters. Submitted. Publications

41 41 Thank You Q and A


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