Electroencephalographic compression based on modulated filter banks and wavelet transform
| Autor: | Fernando Cruz-Roldán, Carlos Bazán-Prieto, Manuel Blanco-Velasco, Julián Cárdenas-Barrera |
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| Předmět: |
Computer science
Polysomnography Wavelet Analysis Data_CODINGANDINFORMATIONTHEORY Lossy compression Wavelet packet decomposition Wavelet Humans Computer vision Quantization (image processing) Signal processing Models Statistical Computers business.industry Quantization (signal processing) Reproducibility of Results Wavelet transform Electroencephalography Signal Processing Computer-Assisted Pattern recognition Data Compression Filter bank Artificial intelligence business Algorithms Software Data compression |
| Zdroj: | Scopus-Elsevier EMBC |
| Popis: | Due to the large volume of information generated in an electroencephalographic (EEG) study, compression is needed for storage, processing or transmission for analysis. In this paper we evaluate and compare two lossy compression techniques applied to EEG signals. It compares the performance of compression schemes with decomposition by filter banks or wavelet Packets transformation, seeking the best value for compression, best quality and more efficient real time implementation. Due to specific properties of EEG signals, we propose a quantization stage adapted to the dynamic range of each band, looking for higher quality. The results show that the compressor with filter bank performs better than transform methods. Quantization adapted to the dynamic range significantly enhances the quality. |
| Databáze: | OpenAIRE |
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