Information theory is applied to data compression in many fields, to efficiently store or transmit texts, sounds, images and signals. In this chapter, different techniques for Electroencephalograph (EEG) and Dynamic or Holter EEG data compression will be discussed, with the requirement that compression should not prevent perfect reconstruction of the original information from the compressed one (such compression techniques are called “lossless”).
The present work was performed in cooperation with the Neurological Department of the Santa Chiara Hospital in Trento where the hardware acquires up to 32 channels, with 8 bit accuracy, at a maximum sampling rate of 1 kHz. However, in everyday practice, a minor number of channels and a lower sampling rate suffice. All results reported are referred to 128 Hz sampling rate per channel, 8 bit accuracy, 20 channels (20,480 bps data stream), which is considered sufficient to achieve a good EEG signal quality. Lossy compressions can preserve...
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Leondes, C.T. (2003). Techniques in Data Compression for Electroencephalograms. In: Leondes, C.T. (eds) Computational Methods in Biophysics, Biomaterials, Biotechnology and Medical Systems. Springer, Boston, MA. https://doi.org/10.1007/0-306-48329-7_38
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DOI: https://doi.org/10.1007/0-306-48329-7_38
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