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Brain Sciences
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Neurophysiological Techniques for Epilepsy
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Neurophysiological Techniques for Epilepsy

A special issue of Brain Sciences (ISSN 2076-3425).

Deadline for manuscript submissions: closed (25 February 2020) | Viewed by 8937

Special Issue Editor

Dr. Jesús Pastor

E-Mail Website
Guest Editor
Clinical Neurophysiology, Hospital Universitario de la Princesa, Madrid, Spain
Interests: epilepsy; deep brain stimulation; intraoperative neurophysiological monitoring; quantified electroencephalography; continous electroencephalography monitoring; network theory; multivariate analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Epilepsy is eminently a bioelectrical pathology. Changes in neurotransmitters, synapses, ion channels or global membrane excitability are among the mechanisms responsible for seizures. Therefore, techniques devoted to analyzing electric brain currents are the main tools available when it comes to studying epilepsy. In the center of these, we have electroencephalography (EEG) and all of techniques derived from it (video-EEG, electrocorticography, etc). Recent developments in numerical analysis have permitted an outburst of works describing exciting pathophysiological explanations (e.g., epileptic network theory) and powerful diagnostic tools (quantified EEG or qEEG). Magnetoencephalography (MEG) and synchronized EEG–magnetic resonance imaging are also promising fields (or consolidated realities). New developments in diagnosis are appearing as wereable devices. However, neurophysiology is not only relevant in the diagnostic side. New approaches, including deep brain stimulation (DBS) and extracranial methods (transcranial magnetic or direct current stimulation), as well as open and closed-loop implanted systems, promise a better control, even for generalized epilepsies. This Special Issue will broadly cover all the pathophysiological, diagnostic, and therapeutic aspects of epilepsy addressed by neurophysiology.

Dr. Jesús Pastor
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. Brain Sciences 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 2200 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

  • quantified electroencephalography
  • network theory
  • numerical analysis
  • brain stimulation

Published Papers (3 papers)

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Research

15 pages, 1431 KiB  
Article
Double-Step Machine Learning Based Procedure for HFOs Detection and Classification
by Nicolina Sciaraffa, Manousos A. Klados, Gianluca Borghini, Gianluca Di Flumeri, Fabio Babiloni and Pietro Aricò
Brain Sci. 2020, 10(4), 220; https://doi.org/10.3390/brainsci10040220 - 08 Apr 2020
Cited by 19 | Viewed by 3203
Abstract
The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This [...] Read more.
The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data. Full article
(This article belongs to the Special Issue Neurophysiological Techniques for Epilepsy)
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19 pages, 3702 KiB  
Article
Quantified EEG for the Characterization of Epileptic Seizures versus Periodic Activity in Critically Ill Patients
by Lorena Vega-Zelaya, Elena Martín Abad and Jesús Pastor
Brain Sci. 2020, 10(3), 158; https://doi.org/10.3390/brainsci10030158 - 10 Mar 2020
Cited by 7 | Viewed by 2851
Abstract
Epileptic seizures (ES) are frequent in critically ill patients and their detection and treatment are mandatory. However, sometimes it is quite difficult to discriminate between ES and non-epileptic bursts of periodic activity (BPA). Our aim was to characterize ES and BPA by means [...] Read more.
Epileptic seizures (ES) are frequent in critically ill patients and their detection and treatment are mandatory. However, sometimes it is quite difficult to discriminate between ES and non-epileptic bursts of periodic activity (BPA). Our aim was to characterize ES and BPA by means of quantified electroencephalography (qEEG). Records containing either ES or BPA were visually identified and divided into 1 s windows that were 10% overlapped. Differential channels were grouped by frontal, parieto-occipital and temporal lobes. For every channel and window, the power spectrum was calculated and the area for delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands and spectral entropy (Se) were computed. Mean values of percentage changes normalized to previous basal activity and standardized mean difference (SMD) for every lobe were computed. We have observed that BPA are characterized by a selective increment of delta activity and decrease in Se along the scalp. Focal seizures (FS) always propagated and were similar to generalized seizures (GS). In both cases, although delta and theta bands increased, the faster bands (alpha and beta) showed the highest increments (more than 4 times) without modifications in Se. We have defined the numerical features of ES and BPA, which can facilitate its clinical identification. Full article
(This article belongs to the Special Issue Neurophysiological Techniques for Epilepsy)
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16 pages, 3336 KiB  
Article
Neurophysiological Characterization of Thalamic Nuclei in Epileptic Anaesthetized Patients
by Lorena Vega-Zelaya, Cristina V. Torres, Marta Navas and Jesús Pastor
Brain Sci. 2019, 9(11), 312; https://doi.org/10.3390/brainsci9110312 - 07 Nov 2019
Cited by 7 | Viewed by 2451
Abstract
Deep brain stimulation (DBS) requires precise localization, which is especially difficult at the thalamus, and even more difficult in anesthetized patients. We aimed to characterize the neurophysiological properties of the ventral intermediate (V.im), ventral caudal (V.c), and centromedian parvo (Ce.pc) and the magnocellular [...] Read more.
Deep brain stimulation (DBS) requires precise localization, which is especially difficult at the thalamus, and even more difficult in anesthetized patients. We aimed to characterize the neurophysiological properties of the ventral intermediate (V.im), ventral caudal (V.c), and centromedian parvo (Ce.pc) and the magnocellular (Ce.mc) thalamic nuclei. We obtained microelectrode recordings from five patients with refractory epilepsy under general anesthesia. Somatosensory evoked potentials recorded by microelectrodes were used to identify the V.c nucleus. Trajectories were reconstructed off-line to identify the nucleus recorded, and the amplitude of the action potential (AP) and the tonic (i.e., mean frequency, density, probability of interspike interval) and phasic (i.e., burst index, pause index, and pause ratio) properties of the pattern discharges were analyzed. The Mahalanobis metric was used to evaluate the similarity of the patterns. The mean AP amplitude was higher for the V.im nucleus (172.7 ± 7.6 µV) than for the other nuclei, and the mean frequency was lower for the Ce.pc nucleus (7.2 ± 0.8 Hz) and higher for the V.c nucleus (11.9 ± 0.8 Hz) than for the other nuclei. The phasic properties showed a bursting pattern for the V.c nucleus and a tonic pattern for the centromedian and V.im nuclei. The Mahalanobis distance was the shortest for the V.im/V.c and Ce.mp/Ce.pc pairs. Therefore, the different properties of the thalamic nuclei, even for patients under general anesthesia, can be used to positively define the recorded structure, improving the exactness of electrode placement in DBS. Full article
(This article belongs to the Special Issue Neurophysiological Techniques for Epilepsy)
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