Title :
Time-varying statistical complexity measures with application to EEG analysis and segmentation
Author :
Celka, P. ; Cold, P.
Author_Institution :
Centre Suisse d´´Electronique et de Microtechnique SA, Neuchatel, Switzerland
Abstract :
The recently proposed instantaneous statistical dimension is compared to new conditional Renyi entropies. The motivation for introducing these time-varying complexity measures is the analysis of electroencephalograms for which nonstationarity is an inherent property. Experimental data from babies are analyzed using the proposed complexity measures. The instantaneous statistical dimension computation is based on an adaptive autocorrelation eigenspectrum computation known as APEX together with a model selection rule. The conditional Renyi entropies are based on time-frequency representation of the signal. It is shown that: 1) the three time-varying complexity measures account for a component counting property, 2) the instantaneous statistical dimension is the most robust to Gaussian white noise.
Keywords :
Gaussian noise; computational complexity; eigenvalues and eigenfunctions; electroencephalography; entropy; feature extraction; medical signal processing; paediatrics; signal representation; statistical analysis; time-frequency analysis; white noise; EEG analysis; EEG segmentation; Gaussian white noise; adaptive autocorrelation eigenspectrum; babies; component counting property; conditional Renyi entropies; feature extraction; instantaneous statistical dimension; model selection rule; time-frequency representation; time-varying statistical complexity; Australia; Biological neural networks; Central nervous system; Eigenvalues and eigenfunctions; Electrodes; Electroencephalography; Entropy; Neurons; Pediatrics; Time frequency analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1020601
