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Biomedicines
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Electroencephalography (EEG) Signal Processing for Epilepsy
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Electroencephalography (EEG) Signal Processing for Epilepsy

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Neurobiology and Clinical Neuroscience".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14296

Special Issue Editor

Prof. Dr. Chou-Ching Lin

E-Mail Website
Guest Editor
Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
Interests: seizure detection; seizure prediction; seizure controllability; EEG modeling; brain computer interface

Special Issue Information

Dear Colleagues,

EEG (electroencephalography) is one of the most important investigational tools for use in clinical practice and in research regarding seizures. In the past, EEG was mostly only viewed using simple signal processing and its interpretation largely depended on personal expertise. However, due to progress in signal processing techniques, more and more features and changes were uncovered and quantified. The correlation between these quantified variables and clinical manifestations has shown the promising possibility of the precise monitoring and prediction of clinical outcomes of seizures. In addition, the combination of mathematical modeling and parameter estimation at different physical scales, including synapses, single neuron, neural population and neural network scales, has opened a new field for the evaluation of microscopic parameters such as receptor dysfunction using a macroscopic measurement, i.e., EEG.

This Special Issue is a timely collection of papers that review and propose new EEG-based techniques to detect and predict seizures (with experimental verification) and to evaluate the efficacy of therapies against seizures. At the same time, the underlying mechanisms of epileptogenicity and factors affecting the temporal progression of seizures can be investigated by means of mathematical modeling and system identification combined with experimental verification. Control strategies can then be designed accordingly.

Prof. Dr. Chou-Ching Lin
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. Biomedicines 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 2600 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

  • seizure detection
  • seizure prediction
  • seizure controllability
  • seizure suppression by electrical stimulation
  • seizure suppression by optogenetic stimulation
  • EEG processing for feedback control of seizure
  • mathematic modeling of EEG
  • system identification of EEG

Published Papers (7 papers)

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Research

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14 pages, 2696 KiB  
Article
MEG Node Degree for Focus Localization: Comparison with Invasive EEG
by Stefan Rampp, Martin Kaltenhäuser, Nadia Müller-Voggel, Arnd Doerfler, Burkhard S. Kasper, Hajo M. Hamer, Sebastian Brandner and Michael Buchfelder
Biomedicines 2023, 11(2), 438; https://doi.org/10.3390/biomedicines11020438 - 02 Feb 2023
Cited by 2 | Viewed by 1413
Abstract
Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may [...] Read more.
Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients. Resting state data were subjected to whole brain, all-to-all connectivity analysis using the imaginary part of coherence. Graphs were described using parcellated ND. SOZ localization was investigated on a lobar and sublobar level. On a lobar level, all frequency bands except alpha showed significantly higher maximal ND (mND) values inside the SOZ compared to outside (ratios 1.11–1.20, alpha 1.02). Area-under-the-curve (AUC) was 0.67–0.78 for all expected alpha (0.44, ns). On a sublobar level, mND inside the SOZ was higher for all frequency bands (1.13–1.38, AUC 0.58–0.78) except gamma (1.02). MEG ND is significantly related to SOZ in delta, theta and beta bands. ND may provide new localization tools for presurgical evaluation of epilepsy surgery. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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15 pages, 3441 KiB  
Article
Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence
by Petroula Laiou, Andrea Biondi, Elisa Bruno, Pedro F. Viana, Joel S. Winston, Zulqarnain Rashid, Yatharth Ranjan, Pauline Conde, Callum Stewart, Shaoxiong Sun, Yuezhou Zhang, Amos Folarin, Richard J. B. Dobson, Andreas Schulze-Bonhage, Matthias Dümpelmann, Mark P. Richardson and RADAR-CNS Consortium
Biomedicines 2022, 10(10), 2662; https://doi.org/10.3390/biomedicines10102662 - 21 Oct 2022
Viewed by 1465
Abstract
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In [...] Read more.
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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17 pages, 4148 KiB  
Article
Using Constrained Square-Root Cubature Kalman Filter for Quantifying the Severity of Epileptic Activities in Mice
by Chih-Hsu Huang, Peng-Hsiang Wang, Ming-Shaung Ju and Chou-Ching K. Lin
Biomedicines 2022, 10(7), 1588; https://doi.org/10.3390/biomedicines10071588 - 03 Jul 2022
Viewed by 1323
Abstract
(1) Background: Quantification of severity of epileptic activities, especially during electrical stimulation, is an unmet need for seizure control and evaluation of therapeutic efficacy. In this study, a parameter ratio derived from constrained square-root cubature Kalman filter (CSCKF) was formulated to quantify the [...] Read more.
(1) Background: Quantification of severity of epileptic activities, especially during electrical stimulation, is an unmet need for seizure control and evaluation of therapeutic efficacy. In this study, a parameter ratio derived from constrained square-root cubature Kalman filter (CSCKF) was formulated to quantify the excitability of local neural network and compared with three commonly used indicators, namely, band power, Teager energy operator, and sample entropy, to objectively determine their effectiveness in quantifying the severity of epileptiform discharges in mice. (2) Methods: A set of one normal and four types of epileptic EEGs was generated by a mathematical model. EEG data of epileptiform discharges during two types of electrical stimulation were recorded in 20 mice. Then, EEG segments of 5 s in length before, during and after the real and sham stimulation were collected. Both simulated and experimental data were used to compare the consistency and differences among the performance indicators. (3) Results: For the experimental data, the results of the four indicators were inconsistent during both types of electrical stimulation, although there was a trend that seizure severity changed with the indicators. For the simulated data, when the simulated EEG segments were used, the results of all four indicators were similar; however, this trend did not match the trend of excitability of the model network. In the model output which retained the DC component, except for the CSCKF parameter ratio, the results of the other three indicators were almost identical to those using the simulated EEG. For CSCKF, the parameter ratio faithfully reflected the excitability of the neural network. (4) Conclusion: For common EEG, CSCKF did not outperform other commonly used performance indicators. However, for EEG with a preserved DC component, CSCKF had the potential to quantify the excitability of the neural network and the associated severity of epileptiform discharges. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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13 pages, 1998 KiB  
Article
Resting-State EEG Functional Connectivity in Children with Rolandic Spikes with or without Clinical Seizures
by Min-Lan Tsai, Chuang-Chin Wang, Feng-Chin Lee, Syu-Jyun Peng, Hsi Chang and Sung-Hui Tseng
Biomedicines 2022, 10(7), 1553; https://doi.org/10.3390/biomedicines10071553 - 29 Jun 2022
Cited by 5 | Viewed by 1601
Abstract
Alterations in dynamic brain network function are increasingly recognized in epilepsy. Benign childhood epilepsy with centrotemporal spikes (BECTS), or benign rolandic seizures, is the most common idiopathic focal epilepsy in children. In this study, we analyzed EEG functional connectivity (FC) among children with [...] Read more.
Alterations in dynamic brain network function are increasingly recognized in epilepsy. Benign childhood epilepsy with centrotemporal spikes (BECTS), or benign rolandic seizures, is the most common idiopathic focal epilepsy in children. In this study, we analyzed EEG functional connectivity (FC) among children with rolandic spikes with or without clinical seizures as compared to controls, to investigate the relationship between FC and clinical parameters in children with rolandic spikes. The FC analysis based on graph theory and network-based statistics in different frequency bands evaluated global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands. Similar to BECTS patients with seizures, children with rolandic spikes without seizures had significantly increased global efficiency, mean clustering coefficient, mean nodal strength, and connectivity strength, specifically in the theta frequency band at almost all proportional thresholds, compared with age-matched controls. Decreased mean betweenness centrality was only present in BECTS patients with seizures. Age at seizure onset was significantly positively associated with the strength of EEG-FC. The decreased function of betweenness centrality was only presented in BECTS patients with clinical seizures, suggesting weaker local connectivity may lower the seizure threshold. These findings may affect treatment policy in children with rolandic spikes. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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17 pages, 2227 KiB  
Article
Multi-Channel Vision Transformer for Epileptic Seizure Prediction
by Ramy Hussein, Soojin Lee and Rabab Ward
Biomedicines 2022, 10(7), 1551; https://doi.org/10.3390/biomedicines10071551 - 29 Jun 2022
Cited by 11 | Viewed by 2888
Abstract
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions [...] Read more.
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28–91.15% for invasive EEG data. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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21 pages, 9096 KiB  
Article
Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection
by Gaetano Zazzaro and Luigi Pavone
Biomedicines 2022, 10(7), 1491; https://doi.org/10.3390/biomedicines10071491 - 23 Jun 2022
Cited by 2 | Viewed by 1571
Abstract
Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive [...] Read more.
Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. Methods: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. Results: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. Conclusions: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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Review

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15 pages, 2029 KiB  
Review
EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis
by Emanuele Cerulli Irelli, Giorgio Leodori, Alessandra Morano and Carlo Di Bonaventura
Biomedicines 2022, 10(10), 2428; https://doi.org/10.3390/biomedicines10102428 - 28 Sep 2022
Cited by 4 | Viewed by 2776
Abstract
Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors [...] Read more.
Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors have been largely validated among IGE patients, the impact of routine electroencephalography (EEG) findings in predicting drug resistance is still controversial and a growing number of authors highlighted the potential importance of capturing the sleep state in this setting. In addition, the development of advanced computational techniques to analyze EEG data has opened new opportunities in the identification of reliable and reproducible biomarkers of drug resistance in IGE patients. In this manuscript, we summarize the EEG findings associated with treatment resistance in IGE by reviewing the results of studies considering standard EEGs, 24-h EEG recordings, and resting-state protocols. We discuss the role of 24-h EEG recordings in assessing seizure recurrence in light of the potential prognostic relevance of generalized fast discharges occurring during sleep. In addition, we highlight new and promising biomarkers as identified by advanced EEG analysis, including hypothesis-driven functional connectivity measures of background activity and data-driven quantitative findings revealed by machine learning approaches. Finally, we thoroughly discuss the methodological limitations observed in existing studies and briefly outline future directions to identify reliable and replicable EEG biomarkers in IGE patients. Full article
(This article belongs to the Special Issue Electroencephalography (EEG) Signal Processing for Epilepsy)
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