Multiple Hybrid Compression Techniques for Electroencephalography Data

The large data size of Electroencephalography (EEG) is a result of long-time recording, the large number of electrodes, and a high sampling rate together. Therefore, the required bandwidth and the storage space are larger for efficient data transmission and storing. So, for higher efficiency transmission with less bandwidth and storage space, EEG data compression is a very important issue. This paper introduces two efficient algorithms for EEG compression. In the first algorithm, the EEG data is transformed through Discrete Wavelet Transform (DWT). Then it passes through Set Partitioning in Hierarchical Trees (SPIHT) compression algorithm. While in the second algorithm the data is segmented into N segments and these segments are transformed using Discrete Cosine Transform (DCT) then encoded using Uniform Quantized Huffman (UQH) scheme. Finally, the Lempel Ziv Welch (LZW) is used as a second lossless encoding algorithm for making a heavy compression. The system performance is evaluated in terms of the total time for compression and reconstruction, compression ratio, and root mean square error. The proposed hybrid technique DCT/UQH/LZW achieves 95% compression compared to 59% by DCT/RLE with the same similarity. Furthermore, it reduces 50% less root mean square error.