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Bioengineering
Special Issues
Recent Advance in Epilepsy and Brain Mapping
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Recent Advance in Epilepsy and Brain Mapping

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 6709

Special Issue Editor

Prof. Dr. Gholam K. Motamedi

E-Mail Website
Guest Editor
Department of Neurology, Georgetown University Medical Center, Washington, DC 20007, USA
Interests: basic mechanisms involved in epileptogenesis; epilepsy surgery; novel treatment modalities for epilepsy; cortical stimulation mapping; direct cortical stimulation to treat seizures; hypothermia as a potential therapeutic modality in epilepsy; high-frequency oscillations (HFOs) and their role in seizure localization and beyond
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the subdural recording of electrical activity has become a critical part of the presurgical evaluation of patients with intractable (refractory) epilepsy. Direct recordings from the brain have not only helped pinpoint the epileptogenic and ictal onset zones, but also tremendously added to our knowledge of the eloquent cortex and the inner workings of the brain in general. These achievements are predominantly owed to subdural grid electrodes placed surgically and seeming to be more suitable for electrical stimulation mapping. With the emergence of the concept of epilepsy as a network disease and, therefore, increasing the usage of stereoelectroencephalography (SEEG), brain mapping using SEEG is becoming a common practice.  The purpose of this Special Issue is to review the latest advances in epilepsy, including both basic and clinical aspects of the disease, with a special focus on intractable epilepsy and cortical mapping, the latest discoveries in molecular and cellular neuroscience, as well as medical and surgical advancements in the field.

Prof. Dr. Gholam K. Motamedi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • epilepsy research
  • epilepsy surgery
  • intractable epilepsy
  • brain mapping
  • stereoelectroencephalography (SEEG)

Published Papers (2 papers)

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Research

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21 pages, 4136 KiB  
Article
Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
by João Matos, Guilherme Peralta, Jolan Heyse, Eric Menetre, Margitta Seeck and Pieter van Mierlo
Bioengineering 2022, 9(11), 690; https://doi.org/10.3390/bioengineering9110690 - 14 Nov 2022
Cited by 3 | Viewed by 1773
Abstract
Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after [...] Read more.
Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient’s cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models’ evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure. Full article
(This article belongs to the Special Issue Recent Advance in Epilepsy and Brain Mapping)
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Review

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35 pages, 2766 KiB  
Review
Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review
by Mohamed Sami Nafea and Zool Hilmi Ismail
Bioengineering 2022, 9(12), 781; https://doi.org/10.3390/bioengineering9120781 - 8 Dec 2022
Cited by 20 | Viewed by 4457
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from [...] Read more.
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field. Full article
(This article belongs to the Special Issue Recent Advance in Epilepsy and Brain Mapping)
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