Permutation Entropy Applied to the Characterization of the Clinical Evolution of Epileptic Patients under PharmacologicalTreatment

Mateos, Diego M.; Díaz, Juan Manuel; Lamberti, Pedro W.

doi:10.3390/e16115668

Cited by 21 publications

(15 citation statements)

References 18 publications

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“…However, if the value is too large, the phase space reconstruction will homogenize the time series, the calculation will be time consuming, and subtle changes in the sequence will not be reflected. The time delay l has little influence on the entropy of the time series (Mateos et al, 2014 ). To allow every possible order pattern of dimension m to occur in a time series of length N , the condition m !…”

Section: Methodsmentioning

confidence: 99%

Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

Wang

Niu

Miao

et al. 2017

Front. Aging Neurosci.

102

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Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.

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Section: Methodsmentioning

confidence: 99%

Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

Wang

Niu

Miao

et al. 2017

Front. Aging Neurosci.

102

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“…Nicolau et al investigated the use of PE as a feature for automated epileptic seizure detection (Nicolaou and Georgiou, 2012 ). Mateos et al developed a method based on PE to characterize EEG from different stages in the treatment of a chronic epileptic patient (Mateos et al, 2014 ). These studies suggest that PE is a useful tool for the study of epilepsy.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Prediction Based on Permutation Entropy

Yang

Zhou

Niu

et al. 2018

Front. Comput. Neurosci.

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Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h−1. The best results with SS of 100% and FPR of 0 h−1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.

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“…The same authors proposed an unsupervised multi-scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals and to identify epileptic zones [11]. Mateos et al [12] developed a method based on PE to characterize EEG from different stages in the treatment of a chronic epileptic patient. Li et al [13] carried out statistical experiments to explore the utility of using relevance feedback on EEG data to distinguish between different activity states in human absence epilepsy.…”

Section: Introductionmentioning

confidence: 99%

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

Mammone

Duun‐Henriksen²,

Kjær

et al. 2015

Entropy

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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.

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Permutation Entropy Applied to the Characterization of the Clinical Evolution of Epileptic Patients under PharmacologicalTreatment

Cited by 21 publications

References 18 publications

Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

Epileptic Seizure Prediction Based on Permutation Entropy

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

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