An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals

2008 ◽  
Vol 12 (5) ◽  
pp. 587-594 ◽  
Author(s):  
N. Sriraam ◽  
C. Eswaran
Keyword(s):  
Lossless Compression ◽  
Error Modeling ◽  
Eeg Signals

scholarly journals A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
N. Sriraam
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
High Performance ◽  
Digital Health ◽  
Error Modeling ◽  
Eeg Signals ◽  
Compression Scheme ◽  
Diagnostic Reliability ◽  
Compression Efficiency ◽  
Eeg Data

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

An Efficient Near-lossless Compression Algorithm for Multichannel EEG signals

2021 ◽  
Author(s):  
Giuseppe Campobello ◽  
Angelica Quercia ◽  
Giovanni Gugliandolo ◽  
Antonino Segreto ◽  
Elisa Tatti ◽  
...  
Keyword(s):  
Lossless Compression ◽  
Compression Algorithm ◽  
Eeg Signals ◽  
Multichannel Eeg

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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