Hybrid Fractal-Wavelet Method for Multi-Channel EEG Signal Compression

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analy...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 33; H. 8; S. 2583 - 2604
Hauptverfasser: Saeedi, Jamal, Faez, Karim, Moradi, Mohammad Hassan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.08.2014
Springer Nature B.V
Schlagworte:
Channels
Circuits and Systems
Coding
Compressing
Electrical Engineering
Electroencephalography
Electronics and Microelectronics
Engineering
Fractal analysis
Fractals
Instrumentation
Longitudinal waves
Optimization
Signal processing
Signal,Image and Speech Processing
Wavelet
Wavelet transforms
Wavelet-based coding
Compression
Multi-objective genetic optimization
Electroencephalograms
Fractal coding
ISSN:0278-081X, 1531-5878
Online-Zugang:Volltext
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Zusammenfassung:In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-014-9764-y