Saturation of electroencephalogram permutation entropy for large time lags

Попов, Антон; Avilov, Oleksii; Kanaykin, Oleksii

doi:10.1109/elnano.2013.6552073

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…In the recent paper [11] the phenomenon of "PE saturation" was highlighted, which consists in the fact that for the large lags the PE value increases, nearly reaching value of 1 after some lag. This holds true for EEG signals from both healthy subjects and from epileptic patients.…”

Section: Introductionmentioning

confidence: 98%

Permutation entropy of EEG signals for different sampling rate and time lag combinations

Попов

Avilov

Kanaykin³

2013

2013 Signal Processing Symposium (SPS)

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Permutation entropy (PE) measure is investigated for the cases when different time lags are used to obtain embedded vectors and the signal under analysis has different sampling rate. PE of orders from 2 to 5 was calculated for the electroencephalogram signals from healthy patients for raw signal and sequence of downsampled signals with different time lags. For various combinations of downsampling factor and time lag the averaged PE plots indicated that PE of a signal appears to be the same for certain combinations. It is defined that PE values would be equal for combinations of integer lags and downsampling factors whose product gives same result. The need of specifying both time lag and sampling rate or time interval between samples in patterns, when studying permutation entropy is emphasized, and this can be extended to other techniques employing the embedded patterns analysis.

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Section: Introductionmentioning

confidence: 98%

Permutation entropy of EEG signals for different sampling rate and time lag combinations

Попов

Avilov

Kanaykin³

2013

2013 Signal Processing Symposium (SPS)

Self Cite

View full text Add to dashboard Cite

show abstract

Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means

Zhu

Wen

et al. 2014

Signal and Image Analysis for Biomedical and Life Sciences

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Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

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Saturation of electroencephalogram permutation entropy for large time lags

Cited by 2 publications

References 12 publications

Permutation entropy of EEG signals for different sampling rate and time lag combinations

Permutation entropy of EEG signals for different sampling rate and time lag combinations

Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means

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