title: Electroencephalographic compression based on modulated filter banks and wavelet transform.
abstract: 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.
authors.affiliation: Departamento de Telecomunicaciones y Electrónica, Universidad Central Marta Abreu de Las Villas, Santa Clara, Cuba.
authors.forename: Carlos
authors.initials: C
authors.lastname: Bazán-Prieto
authors.forename: Julián
authors.initials: J
authors.lastname: Cárdenas-Barrera
authors.forename: Manuel
authors.initials: M
authors.lastname: Blanco-Velasco
authors.forename: Fernando
authors.initials: F
authors.lastname: Cruz-Roldán
mesh_terms: D000465:Algorithms; D003201:Computers; D044962:Data Compression; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D017286:Polysomnography / Q000295:instrumentation / Q000379:methods; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D058067:Wavelet Analysis