Differentiating Interictal and Ictal States in Childhood Absence Epilepsy through Permutation Rényi Entropy

Mammone, Nadia; Duun‐Henriksen, Jonas; Kjær, Troels W.; Morabito, Francesco Carlo

doi:10.3390/e17074627

Cited by 47 publications

(41 citation statements)

References 30 publications

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“…Similar result where shown comparing awake people with people in altered state of consciousness such as sleep, seizure and coma, [11]. Analogous results are shown in other epilepsy syndromes such as absence crises, [12]. In our first case, although there is an interelectrode difference that can be observed in the plane, there is evidence of a common trend in all channels, which shows an approach to the control group, which reflects a change in the functional state of the nervous system that correlates with the favorable evolution after each pharmacological intervention.…”

Section: Discussionsupporting

confidence: 70%

Complexity-Entropy Maps as a Tool for the Characterization of the Clinical Electrophysiological Evolution of Patients Under Pharmacological Treatment with Psychotropic Drugs

Díaz¹,

Mateos²,

Boyallian³

2017

Preprint

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Abstract:In the clinical electrophysiologic practice, the reading and comparing electroencephalographic (EEG) recordings some times is insufficient and take to much time. That is why in the last years it has begun to introduce new methods of EEG analysis, that give a better and faster understanding of the EEG dynamics and allow a rapid intervention in the patient's treatment. Tools coming from the information theory or nonlinear system as an entropy and complexity have been shown to be a very good alternative to address this problem. In this work we introduce a novel method -the permutation Lempel-ziv complexity vs permutation entropy map. This method was applied to EEG of two patients with specific diagnosed pathologies during respective follow up processes of pharmacological changes in order to detect changes that are not evident with the usual inspection method. Our results show that the proposed method are useful for observing an evolutionary retrospective clinical effects of pharmacological interventions in both patients, and from these, to follow the clinical response to the proposed treatment.

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Section: Discussionsupporting

confidence: 70%

Complexity-Entropy Maps as a Tool for the Characterization of the Clinical Electrophysiological Evolution of Patients Under Pharmacological Treatment with Psychotropic Drugs

Díaz¹,

Mateos²,

Boyallian³

2017

Preprint

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“…We evaluate each combination of pattern length D = {3, 4, 5, 6} and time delay τ = {1, 2, 3, 4, 5} to calculate the entropies. For the Renyi entropy, we range the parameter α from 0.25 to 7.5 with 0.25 increments (following [27]), and for the Tsallis Entropy we range the parameter q from 0.1 to 3 with 0.1 increments [40]. In Figure 1, the area under ROC curve calculated by 10-fold cross-validation is plotted (AUC ±1 sd) against the time delay τ.…”

Section: Resultsmentioning

confidence: 99%

“…If the required p i are calculated using the Bandt & Pompe methodology, it leads to the permutation Renyi entropy [27]. Renyi entropy is linked to the probability distribution taken from the time series through α.…”

Section: Brief Review On Permutation Entropy Renyi Permutation Entromentioning

confidence: 99%

Classification of Normal and Pre-Ictal EEG Signals Using Permutation Entropies and a Generalized Linear Model as a Classifier

Redelico

Traversaro

García

et al. 2017

Entropy

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Abstract:In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolization techniques applied to the EEG signal is used to calculate the Tsallis entropy, Shannon Entropy, Renyi Entropy, and Min Entropy, and they are used separately as the only independent variable in a logistic regression model in order to evaluate its capacity as a classification variable in a inferential manner. The area under the Receiver Operating Characteristic (ROC) curve, along with the accuracy, sensitivity, and specificity are used to compare the models. All the permutation entropies are excellent classifiers, with an accuracy greater than 94.5% in every case, and a sensitivity greater than 97%. Accounting for the amplitude in the symbolization technique retains more information of the signal than its counterparts, and it could be a good candidate for automatic classification of EEG signals.

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“…They found a higher PE value for fronto-temporal areas, and it showed a lower value when computed from the parietal/occipital area. In [24], the authors proposed the permutation Rényi entropy (PEr) for the classification of seizure and seizure-free EEG signals of patients diagnosed with childhood absence epilepsy. Their proposed algorithm performed better than conventional PE.…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

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Differentiating Interictal and Ictal States in Childhood Absence Epilepsy through Permutation Rényi Entropy

Cited by 47 publications

References 30 publications

Complexity-Entropy Maps as a Tool for the Characterization of the Clinical Electrophysiological Evolution of Patients Under Pharmacological Treatment with Psychotropic Drugs

Complexity-Entropy Maps as a Tool for the Characterization of the Clinical Electrophysiological Evolution of Patients Under Pharmacological Treatment with Psychotropic Drugs

Classification of Normal and Pre-Ictal EEG Signals Using Permutation Entropies and a Generalized Linear Model as a Classifier

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

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