EEG data compression techniques

• Published in: IEEE transactions on biomedical engineering Vol. 44; no. 2; pp. 105 - 114
• Main Authors: Antoniol, G., Tonella, P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.1997; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Biological and medical sciences; Computational complexity; Computing Methodologies; Costs; Data compression; Decoding; Discrete cosine transforms; Electrodiagnosis. Electric activity recording; Electroencephalography; Electroencephalography - instrumentation; Electroencephalography - methods; Electroencephalography - statistics & numerical data; Encoding; Huffman coding; Humans; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Microcomputers; Microcontrollers; Miscellaneous. Technology; Signal Processing, Computer-Assisted - instrumentation; Vector quantization; Holter recording; Vector quantization; Huffman code; Data compression; Discrete transformation; Electroencephalography; Computational complexity; Transmission time; Optimization; Electrodiagnosis; Compression ratio; Storage capacity; Signal processing; Technique; Computer aid; Comparative study; Information theory
• ISSN: 0018-9294
• DOI: 10.1109/10.552239

Electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed. Data compression permits one to achieve significant reduction in the space required to store signals and in transmission time. The Huffman coding technique in conjunction with derivative computation reaches high compression ratios (on average 49% on Holter and 58% on EEG signals) with low computational complexity. By exploiting this result a simple and fast encoder/decoder scheme capable of real-time performance on a PC was implemented. This simple technique is compared with other predictive transformations, vector quantization, discrete cosine transform (DCT), and repetition count compression methods. Finally, it is shown that the adoption of a collapsed Huffman tree for the encoding/decoding operations allows one to choose the maximum codeword length without significantly affecting the compression ratio. Therefore, low cost commercial microcontrollers and storage devices can be effectively used to store long Holter EEG's in a compressed format.