Journal ArticleDOI
Efficient preprocessing technique for real-time lossless EEG compression
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TLDR
Experimental results show that the preprocessed EEG signal gave improved rate-distortion performance, especially at low bit rates, and less encoding delay compared to the conventional one-dimensional compression scheme.Abstract:
An efficient preprocessing technique of arranging an electroencephalogram (EEG) signal in matrix form is proposed for real-time lossless EEG compression. The compression algorithm consists of an integer lifting wavelet transform as the decorrelator with set partitioning in hierarchical trees as the source coder. Experimental results show that the preprocessed EEG signal gave improved rate-distortion performance, especially at low bit rates, and less encoding delay compared to the conventional one-dimensional compression scheme.read more
Citations
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Journal ArticleDOI
Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?
Justin Dauwels,K.N. Srinivasan,M. Ramasubba Reddy,Toshimitsu Musha,François-Benoît Vialatte,Charles Latchoumane,Jaeseung Jeong,Andrzej Cichocki +7 more
TL;DR: It is shown that strong correlation between slowing and loss of complexity is observed in two independent EEG datasets, and relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects.
Journal ArticleDOI
A Two-Dimensional approach for lossless EEG compression
TL;DR: 2-D based compression schemes yielded higher lossless compression compared to the standard vector-based compression, predictive and entropy coding schemes and were investigated and compared with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding.
Journal ArticleDOI
Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach
TL;DR: In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding, consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual.
Journal ArticleDOI
Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions
TL;DR: A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) based on matrix/tensor decomposition models that achieves attractive compression ratios compared to compressing individual channels separately.
Journal ArticleDOI
Dynamic Dictionary for Combined EEG Compression and Seizure Detection
Hoda Daou,Fabrice Labeau +1 more
TL;DR: The algorithm can positively detect seizure sections in the recordings at bitrates down to 2 bits per sample, and can not only compress EEG channels in one dimension (1-D), but also detect seizure-like activity.
References
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Ralph G. Andrzejak,Klaus Lehnertz,Florian Mormann,Christoph Rieke,Peter David,Christian E. Elger +5 more
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Wavelet Transforms That Map Integers to Integers
TL;DR: Two approaches to build integer to integer wavelet transforms are presented and the precoder of Laroiaet al., used in information transmission, is adapted and combined with expansion factors for the high and low pass band in subband filtering.
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Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm
TL;DR: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed and is significantly more efficient in compression and in computation than previously proposed ECG compression schemes.
Journal ArticleDOI
Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis
Michael D. Adams,F. Kossentni +1 more
TL;DR: At low bit rates, reversible integer-to-integer and conventional versions of transforms were found to often yield results of comparable quality, with the best choice for a given application depending on the relative importance of the preceding criteria.
Journal ArticleDOI
EEG data compression techniques
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.