5658

HHT-based time-frequency analysis method for biomedical signal applications

Chin-Feng Lin, Jin-De Zhu
Department of Electrical Engineering, National Taiwan Ocean University, Taiwan, R.O.C.
Proceedings of the 5th WSEAS international conference on Circuits, systems, signal and telecommunications, CISST ’11, 2011

@inproceedings{lin2011hht,

   title={HHT-based time-frequency analysis method for biomedical signal applications},

   author={Lin, C.F. and Zhu, J.D.},

   booktitle={Proceedings of the 5th WSEAS international conference on Circuits, systems, signal and telecommunications},

   pages={65–68},

   year={2011},

   organization={World Scientific and Engineering Academy and Society (WSEAS)}

}

Download Download (PDF)   View View   Source Source   

740

views

Fourier transform, wavelet transformation, and Hilbert-Huang transformation (HHT) can be used to discuss the frequency characteristics of linear and stationary signals, the time-frequency features of linear and non-stationary signals, the time-frequency features of non-linear and non-stationary signals, respectively [1-6]. HHT is a combination of empirical mode decomposition (EMD) and Hilbert spectral analysis. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signal’s time-frequency-energy distributions. HHT-based time-frequency analysis can be applied to natural physical signals such as earthquake waves, winds, ocean acoustic signals, mechanical diagnosis signals, and biomedical signals. In previous studies, we examined mobile telemedicine, chaos-based medical signal encryption, HHT-based time-frequency analysis of the electroencephalogram (EEG) signals of clinical alcoholics, and sharp wave based HHT time frequency features [7-21]. In this chapter, we discuss the application of HHT-based time-frequency analysis to biomedical signals such as EEG, and electrocardiogram (ECG) signals.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: