A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Abstract

Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.

The Role of Entropy in Construct Specification Equations (CSE) to Improve the Validity of Memory Tests

“…Additionally, both distortion and loss of information in the measurement process—that is, error and uncertainty, respectively—can be modeled in terms of increases in entropy [ 16 ], i.e., disorder. Analogies can be drawn with previously reported entropy-based measurement models, such as the treatment of machine-based signal treatment (e.g., EEG patterns in time series to detect epilepsy [ 17 ]).…”

“…An early form of the Rasch model [ 18 ] posits that the odds ratio of successfully performing a task is equal to the ratio of an ability, h , (Rasch [ 17 ] used the person attribute ’inability’ instead, given by ) to a difficulty, k : …”

“…The total number, P , of messages that can be obtained by distributing the symbols at random over the G cells (with never more than one symbol per cell) is . The information theoretical “Shannon” entropy (as invoked in Figure 1 and Section 2.3 ), which is a measure of the amount of information in these messages, is then given by the classic Brillouin expression [ 17 ]: where K is an arbitrary constant. Stirling’s approximation of ln ( G !)…”

“…. The information theoretical "Shannon" entropy (as invoked in Figure 1 and Section 2.3), which is a measure of the amount of information in these messages, is then given by the classic Brillouin expression [17]:…”

Entropy in Brain Networks

“…Making use of functional resonance imaging (fMRI) and network entropy, microcanonical and canonical ensembles are applied to describe the altered macroscopic properties of the brain network due to AD. A different entropy-based measure of signal irregularity, conditional entropy of ordinal patterns (CEOP), was evaluated as a tool for the diagnosis of epileptic patients by Liu and Fu [ 9 ]. They assessed CEOP using both simulated EEG recordings generated by means of the neural mass model and a real EEG database.…”