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  • Hilbert Huang transform (HHT) is a relatively new method. It seems to be very promising for the different applications in signal processing because it could calculate instantaneous frequency and amplitude which is also important for the biomedical signals. HHT consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in biomedical research. Most traditional data processing methodologies are developed under rigorous mathematic rules and we pay a price for this strict adherence to mathematical rigor. Therefore, data processing has never received the deserved aims as data analysis should, and data processing has never fulfilled its full potential - extracting the information hidden in time series. For example, spectral analysis is synonymous with Fourier-based analysis but the Fourier spectra can only give meaningful interpretation to linear and stationary processes and application to data from nonlinear and nonstationary processes is often problematical but the real world is usually nonlinear and non-stationary. In this paper, 3 examples of this approach are presented, which demonstrate the usefulness of the method. The paper will serve also for an introduction of the method for those who want used this approach in industry, biomedical engineering or in other applications.
  • Hilbert Huang transform (HHT) is a relatively new method. It seems to be very promising for the different applications in signal processing because it could calculate instantaneous frequency and amplitude which is also important for the biomedical signals. HHT consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in biomedical research. Most traditional data processing methodologies are developed under rigorous mathematic rules and we pay a price for this strict adherence to mathematical rigor. Therefore, data processing has never received the deserved aims as data analysis should, and data processing has never fulfilled its full potential - extracting the information hidden in time series. For example, spectral analysis is synonymous with Fourier-based analysis but the Fourier spectra can only give meaningful interpretation to linear and stationary processes and application to data from nonlinear and nonstationary processes is often problematical but the real world is usually nonlinear and non-stationary. In this paper, 3 examples of this approach are presented, which demonstrate the usefulness of the method. The paper will serve also for an introduction of the method for those who want used this approach in industry, biomedical engineering or in other applications. (en)
Title
  • Hilbert-Huang Transform and Its Applications in Engineering and Biomedical Signal Analysis
  • Hilbert-Huang Transform and Its Applications in Engineering and Biomedical Signal Analysis (en)
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  • Hilbert-Huang Transform and Its Applications in Engineering and Biomedical Signal Analysis
  • Hilbert-Huang Transform and Its Applications in Engineering and Biomedical Signal Analysis (en)
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  • RIV/49777513:23220/12:43915668!RIV13-MSM-23220___
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  • wavelet transform; scalogram; modulation; Hilbert-Huang transform; frequency; cardiovascular; biomedical; Amplitude (en)
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  • 1790-5117
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  • WSEAS Press
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  • 978-1-61804-108-1
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  • 23220
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