Book ChapterDOI
Wavelet-Domain L ∞ -Constrained Two-Stage Near-Lossless EEG Coder
K.N. Srinivasan,M. Ramasubba Reddy +1 more
- Vol. 70, pp 76-80
TLDR
Two-stage coder based near-lossless compression of Electroencephalogram (EEG) consists of wavelet based lossy coding layer (until bitplane n d ) followed by entropy coding of the wavelet domain residuals.Abstract:
In this paper, a two-stage coder based near-lossless compression of Electroencephalogram (EEG) is discussed. It consists of wavelet based lossy coding layer (until bitplane n d ) followed by entropy coding of the wavelet domain residuals. L ∞ -error bound is fixed in wavelet domain and the corresponding time-domain absolute error variation is studied. Studies show that intermediate demarcating bit-planes (n d ) register a higher compression and gives a nearly constant time-domain error. Both the normal and epileptic EEG registered a comparable compression performance.read more
Citations
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Journal ArticleDOI
Retained energy-based coding for EEG signals.
TL;DR: A new compression algorithm specifically designed to encode electroencephalographic (EEG) signals is proposed and the results show that the compression scheme yields better compression than other reported methods.
Journal ArticleDOI
Analysis of tractable distortion metrics for EEG compression applications.
TL;DR: The experiments conducted in this paper show that the use of the root-mean-square error as target parameter in EEG compression allows both clinicians and scientists to infer whether coding error is clinically acceptable or not at no cost for the compression ratio.
References
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Giuliano Antoniol,Paolo Tonella +1 more
TL;DR: Electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed and the adoption of a collapsed Huffman tree for the encoding/decoding operations is shown.
Journal ArticleDOI
Context-based lossless and near-lossless compression of EEG signals
N. Memon,Xuan Kong,J. Cinkler +2 more
TL;DR: This work investigates a near lossless compression technique that gives quantitative bounds on the errors introduced during compression and finds that such a technique gives significantly higher compression ratios than lossy compression.
Journal ArticleDOI
Performance Evaluation of Neural Network and Linear Predictors for Near-Lossless Compression of EEG Signals
TL;DR: The proposed near- Lossless scheme facilitates transmission of real time as well as offline EEG signals over network to remote interpretation center economically with less bandwidth utilization compared to other known lossless and near-lossless schemes.