EMD Approach to Multichannel EEG Data - The Amplitude and Phase Synchrony Analysis Technique
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
A chaos-based unequal encryption mechanism in wireless telemedicine with error decryption
WSEAS TRANSACTIONS on SYSTEMS
A DS UWB transmission system for wireless telemedicine
WSEAS TRANSACTIONS on SYSTEMS
Method of Removing Noise from EEG Signals Based on HHT Method
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
An advance wireless multimedia communication application: mobile telemedicine
WSEAS TRANSACTIONS on COMMUNICATIONS
GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia
IEEE Transactions on Information Technology in Biomedicine
Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition
Journal of Signal Processing Systems
Mobile Telemedicine: A Survey Study
Journal of Medical Systems
Analyses of instantaneous frequencies of sharp I, and II electroencephalogram waves for epilepsy
WORLD-EDU'12/CIT'12 Proceedings of the 6th international conference on Communications and Information Technology, and Proceedings of the 3rd World conference on Education and Educational Technologies
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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.