Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

Li, Fali; Liang, Yi; Zhang, Luyan; Yi, Chanlin; Liao, Yuanyuan; Jiang, Yuanling; Si, Yajing; Zhang, Yangsong; Yao, Dezhong; Yu, Liang; Xu, Peng

doi:10.1007/s11571-018-09517-6

Cited by 34 publications

(26 citation statements)

References 48 publications

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“…Individuals suffering from epilepsy have different symptoms, such as unusual sensations, twitching of arms, vision changes, hearing, smelling, or unexpectedly seeing things so that they are unable to perform regular tasks. Although; usually patients do not have any physical symptoms [5,6].…”

Section: Introductionmentioning

confidence: 99%

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

et al. 2021

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Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

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Section: Introductionmentioning

confidence: 99%

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

et al. 2021

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“…Epilepsy is a chronic neurological disease characterized by the paroxysmal seizures that affects people of all ages (Li et al, 2019). According to the WHO, about 50 million people worldwide have epilepsy, making it one of the most common neurological diseases in the world (Perkins, 2019).…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective

Gao

Zhang

et al. 2020

Front. Physiol.

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“…Electroencephalogram (EEG) which is a potential method that can be used not only for detection but also for prediction of epileptic seizures according to extensive evidence (Myers and Kozma, 2018 ; Li et al, 2019 ). EEG does not require open surgery and, thus, is a safe, non-invasive testing procedure that can yield a huge amount of information regarding the health of the patient (Ocak, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures

Aung

Wongsawat

2020

Front. Physiol.

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Epilepsy is one of the most common chronic neurological disorders, and therefore, diagnosis and treatment methods are urgently needed for these patients. Many methods and algorithms that can detect seizures in epileptic patients have been proposed. Electroencephalogram (EEG) is one of helpful tools for investigating epilepsy forms in patients, however, an expert in the neurological field must perform a visual inspection to identify a seizure. Such analyses require longer time because of the huge dataset recorded from many electrodes which are put on the human scalp. With the non-stationary nature of EEG, especially during the abnormality periods, entropy measures gain more interest in the field. In this work, by exploring the advantages of both reliable state-of-the-art entropies, fuzzy entropy and distribution entropy, a modified-Distribution entropy (mDistEn) for epilepsy detection is proposed. As the results, the proposed mDistEn method can successfully achieve the same consistency and better accuracy than using the state-of-the-art entropies. The mDistEn corresponds to higher Area Under the Curve (AUC) values compared with the fuzzy entropy and the distribution entropy and yields 92% classification accuracy.

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Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

Cited by 34 publications

References 48 publications

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective

Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures

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