A statistical framework for EEG channel selection and seizure prediction on mobile

Ibrahim, Fatma E.; El-Gindy, Saly Abd-Elateif; El-Dolil, Sami A.; El‐Fishawy, Adel S.; El-Rabaie, El-Sayed M.; Dessouky, M. I.; El-Dokany, Ibrahim M.; Alotaiby, Turky N.; Alshebeili, Saleh A.; El‐Samie, Fathi E. Abd

doi:10.1007/s10772-018-09565-7

Cited by 39 publications

(17 citation statements)

References 25 publications

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“…40 To leverage the practicality of machine learning in analyzing large and complex datasets, researchers and clinicians have paid great attention to the topic of automatic detection/prediction of epilepsy seizures in EEG recordings. [41][42][43] Various techniques have been applied to this task, including k-nearest neighbor, 44 support vector machine, 45 random forest, 46 and deep learning classifiers. 43,47 Intractability is an unfortunate and substantial issue in about one third of patients with epilepsy.…”

Section: Machine Learningmentioning

confidence: 99%

Can Big Data guide prognosis and clinical decisions in epilepsy?

Cui

Zhang

et al. 2021

Epilepsia

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Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of “‐omics” domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence‐based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision‐making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

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Section: Machine Learningmentioning

confidence: 99%

Can Big Data guide prognosis and clinical decisions in epilepsy?

Cui

Zhang

et al. 2021

Epilepsia

View full text Add to dashboard Cite

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“…In recent studies, Zandi et al [5] have used variational Gaussian Mixture model (GMM), Cui et al [6] have applied extreme learning machines and a specific threshold to differentiate between preictal and interictal classes have been used for classification [7], [11], [14]. Cho et al [13] have used support vector machine as a classifier.…”

Section: Classificationmentioning

confidence: 99%

Epileptic Seizures Prediction Using Deep Learning Techniques

2020

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Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.

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“…It is therefore exceptionally crucial for the accurate automatic detection of epileptic seizures. Epilepsy requires a reliable and accurate strategy to predict seizure events to make the lives of patients less complicated [ 7 , 8 ].…”

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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A statistical framework for EEG channel selection and seizure prediction on mobile

Cited by 39 publications

References 25 publications

Can Big Data guide prognosis and clinical decisions in epilepsy?

Can Big Data guide prognosis and clinical decisions in epilepsy?

Epileptic Seizures Prediction Using Deep Learning Techniques

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

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