Abstract
Permutation entropy (PE) has been widely exploited to measure the complexity of the electroencephalogram (EEG), especially when complexity is linked to diagnostic information embedded in the EEG. Recently, the authors proposed a spatial-temporal analysis of the EEG recordings of absence epilepsy patients based on PE. The goal here is to improve the ability of PE in discriminating interictal states from ictal states in absence seizure EEG. For this purpose, a parametrical definition of permutation entropy is introduced here in the field of epileptic EEG analysis: the permutation Rényi entropy (PEr). PEr has been extensively tested against PE by tuning the involved parameters (order, delay time and alpha). The achieved results demonstrate that PEr outperforms PE, as there is a statistically-significant, wider gap between the PEr levels during the interictal states and PEr levels observed in the ictal states compared to PE. PEr also outperformed PE as the input to a classifier aimed at discriminating interictal from ictal states.
Highlights
Electroencephalography (EEG) is an essential tool for the diagnosis of epilepsy and many other neurological diseases
According to the procedure described in Section 2.2.2, permutation entropy (PE) and permutation Rényi entropy (PEr) are estimated in every possible parameter configuration
We can observe that PEr provided better results, because its (Di, Ri) points lie in the right-upper part of the 2D plot, whereas PE’s point lie in the lower-left part, showing smaller D and R
Summary
Electroencephalography (EEG) is an essential tool for the diagnosis of epilepsy and many other neurological diseases. It generally consists of a non-invasive recording of brain electrical activity by means of electrodes integrated into a cap, which is worn by the patient and connected to the acquisition system through a certain number of wires. CAE affects the characteristics of the EEG and, in particular, its randomness. This is the reason why permutation entropy (PE) was introduced as a possible mathematical tool to process the EEG in order to extract diagnostic information by many researchers. Many authors showed that PE can be successfully used to analyze epileptic EEG
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