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Outline. Introduction to Wavelet Transform Applications of the Discrete Wavelet Transform in Beat Rate Detection DWT Based Beat Rate Detection in ECG Analysis. Improved ECG Signal Analysis Using Wavelet and Feature. Conclusion Reference. Introduction to wavelet transform.

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Outline

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  1. Outline • Introduction to Wavelet Transform • Applications of the Discrete Wavelet Transform in Beat Rate Detection • DWT Based Beat Rate Detection in ECG Analysis. • Improved ECG Signal Analysis Using Wavelet and Feature. • Conclusion • Reference

  2. Introduction to wavelet transform • Fourier transform is the well-known tool for signal processing. • One limitation is that a Fourier transform can’t deal effectively with non-stationary signal. • Short time Fourier transform

  3. Introduction to wavelet transform • Gabor Transform • The mask function is satisfied with Gaussian distribution. • Uncertainly principle • We expected to occur a high resolution in time domain, and then adjust or .

  4. Introduction to wavelet transform • The principle of wavelet transform is based on the concept of STFT and Uncertainly principle. • A mother wavelet . • Scaling and translating . • Sub-wavelets • Fourier transform

  5. Introduction to wavelet transform • Continuous wavelet transform(CWT) • ICWT

  6. Introduction to wavelet transform • Discrete wavelet transform(DWT) • Sub-wavelets • IDWT

  7. DWT applications for beat rate detection DWT Based Beat Rate Detection in ECG Analysis The purpose of this paper is to detect heart beat rate by the concept of discrete wavelet transform, which is suitable for the non stationary ECG signals as it has adeuate scale values and shifting in time.

  8. DWT Based Beat Rate Detection in ECG Analysis ECG(Electrocardiogram) signal

  9. DWT Based Beat Rate Detection in ECG Analysis Preprocessing Denoise Baseline wandering Moving average method and subtraction procedure.

  10. DWT Based Beat Rate Detection in ECG Analysis Preprocessing Denoising : The wavelet transform is used pre-filtering step for subsequent R spike detection by thresholding of the coefficients. Decomposition. Thresholding detail coefficients. Reconstruction.

  11. Feature extraction using DWT Detect R-waves. Thresholding. Positive threshold. Negative threshold. DWT Based Beat Rate Detection in ECG Analysis

  12. DWT applications for beat rate detection Improved ECG Signal Analysis Using Wavelet and Feature. This paper introduced wavelet to extract features and then distinguish several heart beat condition, such as normal beats, atrial premature beats, and premature ventricular contractions.

  13. Improved ECG Signal Analysis Using Wavelet and Feature. • Some kinds of ECG signal: Atrial premature beat Normal beat Premature ventricular contractions

  14. Improved ECG Signal Analysis Using Wavelet and Feature. ECG signal analysis flow

  15. Improved ECG Signal Analysis Using Wavelet and Feature. • Feature Extraction • Matlab : wpdec function, the wavelet ‘bior5.5’.

  16. Improved ECG Signal Analysis Using Wavelet and Feature. • Feature Extraction • Energy • Normal Energy • Entorpy

  17. Improved ECG Signal Analysis Using Wavelet and Feature. • Feature Extraction • Clustering

  18. Improved ECG Signal Analysis Using Wavelet and Feature. wavelet: bior5.5, decomposition level: 1 and 3 with Method 1(●: normal beats, □: atrial premature beats, ○ : premature ventricular contractions) • Method 1

  19. Improved ECG Signal Analysis Using Wavelet and Feature. wavelet: bior5.5, decomposition level: 1 and 3 with Method 2(●: normal beats, □: atrial premature beats, ○ : premature ventricular contractions) • Method 2

  20. Conclusion • Wavelet analysis is widely used in many application. Because it provides both time and frequency information, can overcome the limitation of Fourier transform. • We can learn about the wavelet transform which is able to detect beat rate of signals and to classify the difference of signals. • We also use the wavelet transform on the other beat rate detection.

  21. Reference • Chui, C.K. (1992). An Introduction to Wavelets. Academic Press, San Diego, CA. • S.S. Joshi, C.V. Ghule, "DWT Based Beat Rate Detection in ECG Analysis," Proc. of IEEE International Conference and Workshop on Emerging Trends in Technology(ICWET 2010), pp. 765-769, 2010. • A. Matsuyama, M. Jonkman, F. de Boer, ”Improved ECG Signal Analysis Using Wavelet and Feature Extraction”, Methods Inf. Med.,vol. 46, pp.227-230, 2007

  22. Q&A

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