Publications by authors named "Samden Lhatoo"

128 Publications

NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort Studies.

AMIA Annu Symp Proc 2020 25;2020:1090-1099. Epub 2021 Jan 25.

Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.

: Brain functional connectivity measures are often used to study interactions between brain regions in various neurological disorders such as epilepsy. In particular, functional connectivity measures derived from high resolution electrophysiological signal data have been used to characterize epileptic networks in epilepsy patients. However, existing signal data formats as well as computational methods are not suitable for complex multi-step methods used for processing and analyzing signal data across multiple seizure events. To address the significant data management challenges associated with signal data, we have developed a new workflow-based tool called NeuroIntegrative Connectivity (NIC) using the Cloudwave Signal Format (CSF) as a common data abstraction model. : The NIC compositional workflow-based tool consists of: (1) Signal data processing component for automated pre- processing and generation of CSF files with semantic annotation using epilepsy domain ontology; and (2) Functional network computation component for deriving functional connectivity metrics from signal data analysis across multiple recording channels. The NIC tool streamlines signal data management using a modular software implementation architecture that supports easy extension with new libraries of signal coupling measures and fast data retrieval using a binary search tree indexing structure called NIC-Index. : We evaluated the NIC tool by processing and analyzing signal data for 28 seizure events in two patients with refractory epilepsy. The result shows that certain brain regions have high local measure of connectivity, such as total degree, as compared to other regions during ictal events in both patients. In addition, global connectivity measures, which characterize transitivity and efficiency, increase in value during the initial period of the seizure followed by decrease towards the end of seizure. The NIC tool allows users to efficiently apply several network analysis metrics to study global and local changes in epileptic networks in patient cohort studies.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075544PMC
January 2021

Proceedings of the Sleep and Epilepsy Workshop: Section 3 Mortality: Sleep, Night, and SUDEP.

Epilepsy Curr 2021 Mar 31:15357597211004556. Epub 2021 Mar 31.

Department of Neurology, 1861Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. Likely pathophysiological mechanisms include seizure-induced cardiac and respiratory dysregulation. A frequently identified feature in SUDEP cases is that they occur at night. This raises the question of a role for sleep state in regulating of SUDEP. An association with sleep has been identified in a number of studies with patients and in animal models. The focus of this section of the Sleep and Epilepsy Workshop was on identifying and understanding the role for sleep and time of day in the pathophysiology of SUDEP.
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http://dx.doi.org/10.1177/15357597211004556DOI Listing
March 2021

The Implementation Science for Genomic Health Translation (INSIGHT) Study in Epilepsy: Protocol for a Learning Health Care System.

JMIR Res Protoc 2021 Mar 26;10(3):e25576. Epub 2021 Mar 26.

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States.

Background: Genomic medicine is poised to improve care for common complex diseases such as epilepsy, but additional clinical informatics and implementation science research is needed for it to become a part of the standard of care. Epilepsy is an exemplary complex neurological disorder for which DNA diagnostics have shown to be advantageous for patient care.

Objective: We designed the Implementation Science for Genomic Health Translation (INSIGHT) study to leverage the fact that both the clinic and testing laboratory control the development and customization of their respective electronic health records and clinical reporting platforms. Through INSIGHT, we can rapidly prototype and benchmark novel approaches to incorporating clinical genomics into patient care. Of particular interest are clinical decision support tools that take advantage of domain knowledge from clinical genomics and can be rapidly adjusted based on feedback from clinicians.

Methods: Building on previously developed evidence and infrastructure components, our model includes the following: establishment of an intervention-ready genomic knowledge base for patient care, creation of a health informatics platform and linking it to a clinical genomics reporting system, and scaling and evaluation of INSIGHT following established implementation science principles.

Results: INSIGHT was approved by the Institutional Review Board at the University of Texas Health Science Center at Houston on May 15, 2020, and is designed as a 2-year proof-of-concept study beginning in December 2021. By design, 120 patients from the Texas Comprehensive Epilepsy Program are to be enrolled to test the INSIGHT workflow. Initial results are expected in the first half of 2023.

Conclusions: INSIGHT's domain-specific, practical but generalizable approach may help catalyze a pathway to accelerate translation of genomic knowledge into impactful interventions in patient care.

International Registered Report Identifier (irrid): PRR1-10.2196/25576.
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http://dx.doi.org/10.2196/25576DOI Listing
March 2021

Long-term Home Video EEG for Recording Clinical Events.

J Clin Neurophysiol 2021 Mar;38(2):92-100

Comprehensive Epilepsy Center, Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, U.S.A.

Summary: Around 50 years after the first EEG acquisition by Hans Berger, its use in ambulatory setting was demonstrated. Ever since, ambulatory EEG has been widely available and routinely used in the United States (and to a lesser extent in Europe) for diagnosis and management of patients with epilepsy. This technology alone cannot help with semiological characterization, and absence of video is one of its main drawbacks. Addition of video to ambulatory EEG potentially improves diagnostic yield and opens new aspects of utility for better characterization of patient's events, including differential diagnosis, classification, and quantification of seizure burden. Studies evaluating quality of ambulatory video EEG (aVEEG) suggest good quality recordings are feasible. In the utilization of aVEEG, to maximize yield, it is important to consider pretest probability. Having clear pretest questions and a strong index of suspicion for focal, generalized convulsive or non-epileptic seizures further increases the usefulness of aVEEG. In this article, which is part of the topical issue "Ambulatory EEG," the authors compare long-term home aVEEG to inpatient video EEG monitoring, discuss aVEEG's use in diagnosis and follow-up of patients, and present the authors' own experience of the utility of aVEEG in a teaching hospital setting.
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http://dx.doi.org/10.1097/WNP.0000000000000746DOI Listing
March 2021

Utility of Ambulatory Surface Electroencephalography.

J Clin Neurophysiol 2021 Mar;38(2):75-76

Mayo Clinic, Jacksonville, Florida, U.S.A.

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http://dx.doi.org/10.1097/WNP.0000000000000747DOI Listing
March 2021

Seizure Clusters, Seizure Severity Markers, and SUDEP Risk.

Front Neurol 2021 12;12:643916. Epub 2021 Feb 12.

National Institute of Neurological Disorders and Stroke Center for Sudden Unexpected Death in Epilepsy Research (CSR), McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States.

Seizure clusters may be related to Sudden Unexpected Death in Epilepsy (SUDEP). Two or more generalized convulsive seizures (GCS) were captured during video electroencephalography in 7/11 (64%) patients with monitored SUDEP in the MORTEMUS study. It follows that seizure clusters may be associated with epilepsy severity and possibly with SUDEP risk. We aimed to determine if electroclinical seizure features worsen from seizure to seizure within a cluster and possible associations between GCS clusters, markers of seizure severity, and SUDEP risk. Patients were consecutive, prospectively consented participants with drug-resistant epilepsy from a multi-center study. Seizure clusters were defined as two or more GCS in a 24-h period during the recording of prolonged video-electroencephalography in the Epilepsy monitoring unit (EMU). We measured heart rate variability (HRV), pulse oximetry, plethysmography, postictal generalized electroencephalographic suppression (PGES), and electroencephalography (EEG) recovery duration. A linear mixed effects model was used to study the difference between the first and subsequent seizures, with a level of significance set at < 0.05. We identified 112 GCS clusters in 105 patients with 285 seizures. GCS lasted on average 48.7 ± 19 s (mean 49, range 2-137). PGES emerged in 184 (64.6%) seizures and postconvulsive central apnea (PCCA) was present in 38 (13.3%) seizures. Changes in seizure features from seizure to seizure such as seizure and convulsive phase durations appeared random. In grouped analysis, some seizure features underwent significant deterioration, whereas others improved. Clonic phase and postconvulsive central apnea (PCCA) were significantly shorter in the fourth seizure compared to the first. By contrast, duration of decerebrate posturing and ictal central apnea were longer. Four SUDEP cases in the cluster cohort were reported on follow-up. Seizure clusters show variable changes from seizure to seizure. Although clusters may reflect epilepsy severity, they alone may be unrelated to SUDEP risk. We suggest a stochastic nature to SUDEP occurrence, where seizure clusters may be more likely to contribute to SUDEP if an underlying progressive tendency toward SUDEP has matured toward a critical SUDEP threshold.
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http://dx.doi.org/10.3389/fneur.2021.643916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907515PMC
February 2021

A Bespoke Electronic Health Record for Epilepsy Care (EpiToMe): Development and Qualitative Evaluation.

J Med Internet Res 2021 02 12;23(2):e22939. Epub 2021 Feb 12.

Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States.

Background: While electronic health records (EHR) bring various benefits to health care, EHR systems are often criticized as cumbersome to use, failing to fulfill the promise of improved health care delivery with little more than a means of meeting regulatory and billing requirements. EHR has also been recognized as one of the contributing factors for physician burnout.

Objective: Specialty-specific EHR systems have been suggested as an alternative approach that can potentially address challenges associated with general-purpose EHRs. We introduce the Epilepsy Tracking and optimized Management engine (EpiToMe), an exemplar bespoke EHR system for epilepsy care. EpiToMe uses an agile, physician-centered development strategy to optimize clinical workflow and patient care documentation. We present the design and implementation of EpiToMe and report the initial feedback on its utility for physician burnout.

Methods: Using collaborative, asynchronous data capturing interfaces anchored to a domain ontology, EpiToMe distributes reporting and documentation workload among technicians, clinical fellows, and attending physicians. Results of documentation are transmitted to the parent EHR to meet patient care requirements with a push of a button. An HL7 (version 2.3) messaging engine exchanges information between EpiToMe and the parent EHR to optimize clinical workflow tasks without redundant data entry. EpiToMe also provides live, interactive patient tracking interfaces to ease the burden of care management.

Results: Since February 2019, 15,417 electroencephalogram reports, 2635 Epilepsy Monitoring Unit daily reports, and 1369 Epilepsy Monitoring Unit phase reports have been completed in EpiToMe for 6593 unique patients. A 10-question survey was completed by 11 (among 16 invited) senior clinical attending physicians. Consensus was found that EpiToMe eased the burden of care documentation for patient management, a contributing factor to physician burnout.

Conclusions: EpiToMe offers an exemplar bespoke EHR system developed using a physician-centered design and latest advancements in information technology. The bespoke approach has the potential to ease the burden of care management in epilepsy. This approach is applicable to other clinical specialties.
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http://dx.doi.org/10.2196/22939DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910122PMC
February 2021

Electrical Stimulation-Induced Seizures and Breathing Dysfunction: A Systematic Review of New Insights Into the Epileptogenic and Symptomatogenic Zones.

Front Hum Neurosci 2020 22;14:617061. Epub 2021 Jan 22.

Department of Neurology, University of Texas Health Sciences Center at Houston, Houston, TX, United States.

Electrical stimulation (ES) potentially delineates epileptogenic cortex through induction of typical seizures. Although frequently employed, its value for epilepsy surgery remains controversial. Similarly, ES is used to identify symptomatogenic zones, but with greater success and a long-standing evidence base. Recent work points to new seizure symptoms such as ictal central apnea (ICA) that may enhance presurgical hypotheses. The aims of this review are 2-fold: to determine the value of ES-induced seizures (ESIS) in epilepsy surgery and to analyze current evidence on ICA as a new surrogate of symptomatogenic cortex. Three databases were searched for ESIS. Investigators independently selected studies according to pre-specified criteria. Studies reporting postoperative outcome in patients with ESIS were included in a meta-analysis. For ES-induced apnea, a thorough search was performed and reference list searching was employed. Of 6,314 articles identified for ESIS, 25 were considered eligible to be reviewed in full text. Fourteen studies were included in the qualitative synthesis (1,069 patients); six studies were included in the meta-analysis (530 patients). The meta-analysis showed that favorable outcome is associated with ESIS prior to surgery (OR: 2.02; 95% CI: 1.332-3.08). In addition, the overall estimation of the occurrence of favorable outcome among cases with ESIS is 68.13% (95% CI: 56.62-78.7). On the other hand, recent studies have shown that stimulation of exclusively mesial temporal lobe structures elicits central apnea and represents symptomatogenic anatomic substrates of ICA. This is in variance with traditional teaching that mesial temporal ES is non-symptomatogenic. ES is a tool highly likely to aid in the delineation of the epileptogenic zone, since ESIS is associated with favorable postoperative outcomes (Engel I). There is an urgent need for prospective evaluation of this technique, including effective stimulation parameters and surgical outcomes, that will provide knowledge base for practice. In addition, ES-induced apnea studies suggest that ICA, especially when it is the first or only clinical sign, is an important semiological feature in localizing the symptomatogenic zone to mesial temporal lobe structures, which must be considered in SEEG explorations where this is planned, and in surgical resection strategies.
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http://dx.doi.org/10.3389/fnhum.2020.617061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862564PMC
January 2021

Can Big Data guide prognosis and clinical decisions in epilepsy?

Epilepsia 2021 Mar 2;62 Suppl 2:S106-S115. Epub 2021 Feb 2.

Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA.

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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http://dx.doi.org/10.1111/epi.16786DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011949PMC
March 2021

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data.

Front Integr Neurosci 2020 12;14:491403. Epub 2021 Jan 12.

Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States.

A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.
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http://dx.doi.org/10.3389/fnint.2020.491403DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835283PMC
January 2021

A lightweight convolutional neural network for assessing an EEG risk marker for sudden unexpected death in epilepsy.

BMC Med Inform Decis Mak 2020 12 24;20(Suppl 12):329. Epub 2020 Dec 24.

Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Background: Convolutional neural network (CNN) has achieved state-of-art performance in many electroencephalogram (EEG) related studies. However, the application of CNN in prediction of risk factors for sudden unexpected death in epilepsy (SUDEP) remains as an underexplored area. It is unclear how the trade-off between computation cost and prediction power varies with changes in the complexity and depth of neural nets.

Methods: The purpose of this study was to explore the feasibility of using a lightweight CNN to predict SUDEP. A total of 170 patients were included in the analyses. The CNN model was trained using clips with 10-s signals sampled from the original EEG. We implemented Hann function to smooth the raw EEG signal and evaluated its effect by choosing different strength of denoising filter. In addition, we experimented two variations of the proposed model: (1) converting EEG input into an "RGB" format to address EEG channels underlying spatial correlation and (2) incorporating residual network (ResNet) into the bottle neck position of the proposed structure of baseline CNN.

Results: The proposed baseline CNN model with lightweight architecture achieved the best AUC of 0.72. A moderate noise removal step facilitated the training of CNN model by ensuring stability of performance. We did not observe further improvement in model's accuracy by increasing the strength of denoising filter.

Conclusion: Post-seizure slow activity in EEG is a potential marker for SUDEP, our proposed lightweight architecture of CNN achieved satisfying trade-off between efficiently identifying such biomarker and computational cost. It also has a flexible interface to be integrated with different variations in structure leaving room for further improvement of the model's performance in automating EEG signal annotation.
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http://dx.doi.org/10.1186/s12911-020-01310-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758925PMC
December 2020

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

BMC Med Inform Decis Mak 2020 12 24;20(Suppl 12):328. Epub 2020 Dec 24.

School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.
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http://dx.doi.org/10.1186/s12911-020-01306-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758923PMC
December 2020

Learning to detect the onset of slow activity after a generalized tonic-clonic seizure.

BMC Med Inform Decis Mak 2020 12 24;20(Suppl 12):330. Epub 2020 Dec 24.

School of Biomedical Informatics, UT Health, 7000 Fannin St Suite 600, Houston, TX, USA.

Background: Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8-17% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still not clear what are the physio-/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions https://sbmi.uth.edu/hackathon/archive/sept19.htm .

Methods: In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic-clonic seizure, as well as other EEG signal detection problems exhibiting data paucity.

Results: We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence.

Conclusions: Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.
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http://dx.doi.org/10.1186/s12911-020-01308-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758937PMC
December 2020

Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment.

BMC Med Inform Decis Mak 2020 12 24;20(Suppl 12):326. Epub 2020 Dec 24.

Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Background: Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021-1031, 2008; Nashef in Epilepsia 38:6-8, 1997).

Methods: This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687-5265, 2011).

Results: The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity.

Conclusion: The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient's SUDEP risk.
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http://dx.doi.org/10.1186/s12911-020-01309-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758934PMC
December 2020

Automated detection of activity onset after postictal generalized EEG suppression.

BMC Med Inform Decis Mak 2020 12 24;20(Suppl 12):327. Epub 2020 Dec 24.

School of Biomedical Informatics, UT Health, 7000 Fannin St Suite 600, Houston, TX, USA.

Background: Sudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization.

Methods: We address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording.

Results: We evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES.

Conclusion: Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES-otherwise difficult to detect due to EEG noise and artifacts-using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.
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http://dx.doi.org/10.1186/s12911-020-01307-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758926PMC
December 2020

Association of Peri-ictal Brainstem Posturing With Seizure Severity and Breathing Compromise in Patients With Generalized Convulsive Seizures.

Neurology 2021 01 2;96(3):e352-e365. Epub 2020 Dec 2.

From the NINDS Center for SUDEP Research (L.V., N.L., S.O., M.O.-U., S.T., M.R.S.R., R.K.S., D.F., M.N., C.S., L.A., B.K.G., J.S.H., S.S., J.O., R.M.H., B.D., L.M.B., O.D., G.B.R., P.R., G.-Q.Z., S.D.L.) and Department of Neurology (L.V., N.L., J.P.H., S.O., M.O.-U., S.T., M.R.S.R., N.J.H., J.S.H., G.-Q.Z., S.D.L.), McGovern Medical School, and Biostatistics and Epidemiology Research Design Core (L.Z., G.B.R.), Division of Clinical and Translational Sciences, University of Texas Health Science Center at Houston; Departament de Medicina (L.V.), Universitat Autonoma de Barcelona, Spain; University of Iowa Carver College of Medicine (R.K.S., B.K.G.), Iowa City; NYU Langone School of Medicine (D.F., O.D.), New York; Sidney Kimmel Medical College (M.N.), Thomas Jefferson University, Philadelphia, PA; Division of Pulmonary (K.S.), Critical Care and Sleep Medicine, University Hospitals Medical Center, Cleveland, OH; Institute of Neurology (C.S., L.A., B.D.), University College London, UK; Case Western Reserve University (N.S., X.Z., V.R.-M.), Cleveland, OH; Feinberg School of Medicine (S.S.), Northwestern University, Chicago, IL; Department of Neurobiology and the Brain Research Institute (J.O., R.M.H.), University of California, Los Angeles; Department of Neurology (L.M.B.), Columbia University, New York, NY; and Department of Clinical Neuroscience (P.R.), Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.

Objective: To analyze the association between peri-ictal brainstem posturing semiologies with postictal generalized electroencephalographic suppression (PGES) and breathing dysfunction in generalized convulsive seizures (GCS).

Methods: In this prospective, multicenter analysis of GCS, ictal brainstem semiology was classified as (1) decerebration (bilateral symmetric tonic arm extension), (2) decortication (bilateral symmetric tonic arm flexion only), (3) hemi-decerebration (unilateral tonic arm extension with contralateral flexion) and (4) absence of ictal tonic phase. Postictal posturing was also assessed. Respiration was monitored with thoracoabdominal belts, video, and pulse oximetry.

Results: Two hundred ninety-five seizures (180 patients) were analyzed. Ictal decerebration was observed in 122 of 295 (41.4%), decortication in 47 of 295 (15.9%), and hemi-decerebration in 28 of 295 (9.5%) seizures. Tonic phase was absent in 98 of 295 (33.2%) seizures. Postictal posturing occurred in 18 of 295 (6.1%) seizures. PGES risk increased with ictal decerebration (odds ratio [OR] 14.79, 95% confidence interval [CI] 6.18-35.39, < 0.001), decortication (OR 11.26, 95% CI 2.96-42.93, < 0.001), or hemi-decerebration (OR 48.56, 95% CI 6.07-388.78, < 0.001). Ictal decerebration was associated with longer PGES ( = 0.011). Postictal posturing was associated with postconvulsive central apnea (PCCA) ( = 0.004), longer hypoxemia ( < 0.001), and Spo recovery ( = 0.035).

Conclusions: Ictal brainstem semiology is associated with increased PGES risk. Ictal decerebration is associated with longer PGES. Postictal posturing is associated with a 6-fold increased risk of PCCA, longer hypoxemia, and Spo recovery. Peri-ictal brainstem posturing may be a surrogate biomarker for GCS severity identifiable without in-hospital monitoring.

Classification Of Evidence: This study provides Class III evidence that peri-ictal brainstem posturing is associated with the GCS with more prolonged PGES and more severe breathing dysfunction.
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http://dx.doi.org/10.1212/WNL.0000000000011274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884980PMC
January 2021

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.

Epilepsia 2020 09 7;61(9):1869-1883. Epub 2020 Aug 7.

Department of Clinical Neurosciences, University of Calgary, Calgary, Canada.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
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http://dx.doi.org/10.1111/epi.16633DOI Listing
September 2020

Peri-ictal hypoxia is related to extent of regional brain volume loss accompanying generalized tonic-clonic seizures.

Epilepsia 2020 08 19;61(8):1570-1580. Epub 2020 Jul 19.

Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK.

Objectives: Hypoxia, or abnormally low blood-oxygen levels, often accompanies seizures and may elicit brain structural changes in people with epilepsy which contribute to central processes underlying sudden unexpected death in epilepsy (SUDEP). The extent to which hypoxia may be related to brain structural alterations in this patient group remains unexplored.

Methods: We analyzed high-resolution T1-weighted magnetic resonance imaging (MRI) to determine brain morphometric and volumetric alterations in people with generalized tonic-clonic seizures (GTCS) recorded during long-term video-electroencephalography (VEEG), recruited from two sites (n = 22), together with data from age- and sex-matched healthy controls (n = 43). Subjects were sub-divided into those with mild/moderate (GTCS-hypox-mild/moderate, n = 12) and severe (GTCS-hypox-severe, n = 10) hypoxia, measured by peripheral oxygen saturation (SpO ) during VEEG. Whole-brain voxel-based morphometry (VBM) and regional volumetry were used to assess group comparisons and correlations between brain structural measurements as well as the duration and extent of hypoxia during GTCS.

Results: Morphometric and volumetric alterations appeared in association with peri-GTCS hypoxia, including volume loss in the periaqueductal gray (PAG), thalamus, hypothalamus, vermis, cerebellum, parabrachial pons, and medulla. Thalamic and PAG volume was significantly reduced in GTCS patients with severe hypoxia compared with GTCS patients with mild/moderate hypoxia. Brainstem volume loss appeared in both hypoxia groups, although it was more extensive in those with severe hypoxia. Significant negative partial correlations emerged between thalamic and hippocampal volume and extent of hypoxia, whereas vermis and accumbens volumes declined with increasing hypoxia duration.

Significance: Brain structural alterations in patients with GTCS are related to the extent of hypoxia in brain sites that serve vital functions. Although the changes are associative only, they provide evidence of injury to regulatory brain sites related to respiratory manifestations of seizures.
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http://dx.doi.org/10.1111/epi.16615DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496610PMC
August 2020

Temporal phenotyping for transitional disease progress: An application to epilepsy and Alzheimer's disease.

J Biomed Inform 2020 07 18;107:103462. Epub 2020 Jun 18.

School of Biomedical Informatics, UTHealth, Houston, TX, United States.

Complicated multifactorial diseases deteriorate from one disease to other diseases. For example, existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without. It is important to understand the progress of disease that deteriorates to severe diseases. To this end, we develop a transitional phenotyping method based on both longitudinal and cross-sectional relationships between diseases and/or medications. For a cross-sectional approach, we utilized a skip-gram model to represent co-occurred disease or medication. For a longitudinal approach, we represented each patient as a transition probability between medical events and used supervised tensor factorization to decompose into groups of medical events that develop together. Then we harmonized both information to derive high-risk transitional patterns. We applied our method to disease progress from epilepsy to AD. An epilepsy-AD cohort of 600,000 patients were extracted from Cerner Health Facts data. Our experimental results suggested a causal relationship between epilepsy and later onset of AD, and also identified five epilepsy subgroups with distinct phenotypic patterns leading to AD. While such findings are preliminary, the proposed method combining representation learning with tensor factorization seems to be an effective approach for risk factor analysis.
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http://dx.doi.org/10.1016/j.jbi.2020.103462DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374015PMC
July 2020

Ictal quantitative surface electromyography correlates with postictal EEG suppression.

Neurology 2020 06 12;94(24):e2567-e2576. Epub 2020 May 12.

From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.

Objective: To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment.

Methods: Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES.

Results: We found significant correlations between quantitative surface EMG parameters and the duration of PGES ( < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%.

Conclusions: Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk.

Classification Of Evidence: This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.
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http://dx.doi.org/10.1212/WNL.0000000000009492DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455333PMC
June 2020

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

AMIA Annu Symp Proc 2019 4;2019:1111-1120. Epub 2020 Mar 4.

University of Texas Health Science Center at Houston, Houston, TX 77030.

Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the data for developing computational methods) seizure signal datasets with associated tools for seizure data management and visualization. We introduce SeizureBank, a web-based data management and visualization system for epileptic seizures. SeizureBank comes with a built-in seizure data preparation pipeline and web-based interfaces for querying, exporting and visualizing seizure-related signal data. In this pilot study, 224 seizures from 115 patients were extracted from over one terabyte of signal data and deposited in SeizureBank. To demonstrate the value of this approach, we develop a feature-based seizure identification approach and evaluate the performance on a variety of data sources. The results can serve as a cross-dataset evaluation benchmark for future seizure identification studies.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153150PMC
August 2020

Knowledge and attitudes about sudden death in epilepsy among people living with epilepsy and their healthcare providers in Mulago Hospital, Uganda: A cross-sectional study.

Epilepsia Open 2020 Mar 26;5(1):80-85. Epub 2019 Dec 26.

Department of Medicine College of Health Sciences Makerere University Kampala Uganda.

Objective: The objective of the study was to assess level of knowledge and attitudes of SUDEP among people living with epilepsy (PLWE) and healthcare workers providing epilepsy care in Uganda.

Methods: This cross-sectional study of 48 PLWE and 19 epilepsy care providers used a tailored questionnaire to evaluate epilepsy and SUDEP knowledge, frequency of SUDEP discussion, reasons for not discussing SUDEP, timing of SUDEP discussions, and perceived patient reactions to being provided information on SUDEP.

Results: Median PLWE sample age was 25 (IQR; 19-34) years, 10 (20.8%) were male, median age of onset of epilepsy 12 (IQR; 6-18) years. Half of the PLWE reported that they had never heard of SUDEP. Most PLWE desired detailed information regarding SUDEP and preferred this information during the subsequent visits. Healthcare provider sample mean age was 35.7 (22.8) years, 12 (63.2%) were male and composed of 4 physicians (21.1%). Only 15% (3/20) of providers discussed SUDEP with their patients while 85% (17/20) have never discussed it. The main reasons for not discussing SUDEP were not knowing enough about SUDEP (89.5%) and no adequate support network available (30%). Providers that discussed SUDEP (100%) reported that negative reactions were the most common patient response.

Significance: In this Ugandan sample, most PLWE are not aware of SUDEP and epilepsy care providers rarely discuss SUDEP with their patients or patient caregivers. Negative reactions to SUDEP discussions are common but not universal. There is an urgent need for epilepsy educational programs in clinics and targeted communities addressing SUDEP.
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http://dx.doi.org/10.1002/epi4.12374DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7049805PMC
March 2020

Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach.

JMIR Med Inform 2020 Feb 14;8(2):e17061. Epub 2020 Feb 14.

School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.

Background: Sudden unexpected death in epilepsy (SUDEP) is second only to stroke in neurological events resulting in years of potential life lost. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a period of suppressed brain activity often occurring after generalized tonic-clonic seizure, a most significant risk factor for SUDEP. Therefore, PGES has been considered as a potential biomarker for SUDEP risk. Automatic PGES detection tools can address the limitations of labor-intensive, and sometimes inconsistent, visual analysis. A successful approach to automatic PGES detection must overcome computational challenges involved in the detection of subtle amplitude changes in EEG recordings, which may contain physiological and acquisition artifacts.

Objective: This study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units.

Methods: We used a combination of temporal, frequency, wavelet, and interchannel correlation features derived from EEG signals to train a random forest classifier. We also constructed and applied confidence-based correction rules based on PGES state changes. Motivated by practical utility, we introduced a new, time distance-based evaluation method for assessing the performance of PGES detection algorithms.

Results: The time distance-based evaluation showed that our approach achieved a 5-second tolerance-based positive prediction rate of 0.95 for artifact-free signals. For signals with different artifact levels, our prediction rates varied from 0.68 to 0.81.

Conclusions: We introduced a feature-based, random forest approach for automatic PGES detection using multichannel EEG recordings. Our approach achieved increasingly better time distance-based performance with reduced signal artifact levels. Further study is needed for PGES detection algorithms to perform well irrespective of the levels of signal artifacts.
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http://dx.doi.org/10.2196/17061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055778PMC
February 2020

Collection and Analysis of Multimodal Data for SUDEP Biomarker Discovery.

IEEE Sens Lett 2019 Jan 9;3(1). Epub 2018 Nov 9.

Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.

This paper discusses the acquisition and processing of multimodal physiological data from patients with epilepsy in Epilepsy Monitoring Units for the discovery of risk factors for Sudden Expected Death in Epilepsy (SUDEP) that can be combined through integrative analysis for biomarker discovery.
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http://dx.doi.org/10.1109/LSENS.2018.2880594DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822612PMC
January 2019

Outcome of lesional epilepsy surgery: Report of the first comprehensive epilepsy program in Iran.

Neurol Clin Pract 2019 Aug;9(4):286-295

Kashani Comprehensive Epilepsy Center (JMH, MZ), Kashani Hospital, School of Medicine, Isfahan University of Medical Sciences; Departments of Neurology (JMH, SB, BZ, NM, MZ), Isfahan Neurosciences Research Center and Neurosurgery (HM), Department of Radiology (RB), Students' Research Center (SB, NM), and Department of Psychiatry (MB), Psychosomatic Research Center, School of Medicine, Isfahan University of Medical Sciences; Shefa Neuroscience Research Center (ER), Tehran, Iran; Students' Research Center (AMH), School of Medicine, Shahrekord University of Medical Sciences, Iran; Department of Neurology (PM), University of Tennessee Health Science Center, Memphis, TN; Department of Clinical Neurosciences (YA), University of Calgary, Calgary, Alberta, Canada; and Epilepsy Center (SA, SL), Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH.

Background: We investigated the utility of epilepsy surgery and postoperative outcome in patients with lesional epilepsy in Iran, a relatively resource-poor setting.

Methods: This prospective longitudinal study was conducted during 2007-2017 in Kashani Comprehensive Epilepsy Center, Isfahan, Iran. Patients with a diagnosis of intractable focal epilepsy, with MRI lesions, who underwent epilepsy surgery and were followed up ≥ 24 months, were included and evaluated for postoperative outcome.

Results: A total of 214 patients, with a mean age of 26.90 ± 9.82 years (59.8% men) were studied. Complex partial seizure was the most common type of seizure (85.9%), and 54.2% of the cases had auras. Temporal lobe lesions (75.2%) and mesial temporal sclerosis (48.1%) were the most frequent etiologies. With a mean follow-up of 62.17 ± 19.33 months, 81.8% of patients became seizure-free postoperatively. Anticonvulsants were reduced in 86% of the cases and discontinued in 40.7%. In keeping with previous studies, we found that seizure freedom rates were lower among patients with longer follow-up periods.

Conclusions: We found high rates of seizure freedom after surgery in lesional epilepsy patients despite limited facilities and infrastructure; antiepileptic medications were successfully tapered in almost half of the patients. Considering the favorable outcome of epilepsy surgery in our series, we believe that it is a major treatment option, even in less resource-intensive settings, and should be encouraged. Strategies to allow larger scale utility of epilepsy surgery in such settings in the developing world and dissemination of such knowledge may be considered an urgent clinical need, given the established mortality and morbidity in refractory epilepsy.
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http://dx.doi.org/10.1212/CPJ.0000000000000627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745744PMC
August 2019

Postictal serotonin levels are associated with peri-ictal apnea.

Neurology 2019 10 4;93(15):e1485-e1494. Epub 2019 Sep 4.

From the Department of Neurology (A.M.), Case Western Reserve University; Department of Neurology (M.R.S.R., L.V., N.L., J.P.H., S.D.L.), McGovern Medical School, University of Texas Health Science Center at Houston; Department of Pharmacology and Neurology (C.L.F.), Southern Illinois University School of Medicine, Springfield; Department of Neurology (D.F., O.D.), New York University School of Medicine, New York; Department of Neurology (R.K.S., G.R.), University of Iowa Carver College of Medicine, Iowa City; Department of Neurology (S.S.), Northwestern University Feinberg School of Medicine, Chicago, IL; Institute of Neurology (B.D.), University College London, UK; Department of Neurology (M.N.), Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA; Department of Neurobiology (R.M.H.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Department of Neurology (L.M.B.), Columbia University Medical Center, New York, NY; and Center for SUDEP Research (M.R.S.R., L.V., N.L., D.F., O.D., R.K.S., S.S., B.D., M.N., R.M.H., L.M.B., G.R., S.D.L.), National Institute for Neurological Disorders and Stroke, Bethesda, MD.

Objective: To determine the relationship between serum serotonin (5-HT) levels, ictal central apnea (ICA), and postconvulsive central apnea (PCCA) in epileptic seizures.

Methods: We prospectively evaluated video EEG, plethysmography, capillary oxygen saturation (SpO), and ECG for 49 patients (49 seizures) enrolled in a multicenter study of sudden unexpected death in epilepsy (SUDEP). Postictal and interictal venous blood samples were collected after a clinical seizure for measurement of serum 5-HT levels. Seizures were classified according to the International League Against Epilepsy 2017 seizure classification. We analyzed seizures with and without ICA (n = 49) and generalized convulsive seizures (GCS) with and without PCCA (n = 27).

Results: Postictal serum 5-HT levels were increased over interictal levels for seizures without ICA ( = 0.01), compared to seizures with ICA ( = 0.21). In patients with GCS without PCCA, serum 5-HT levels were increased postictally compared to interictal levels ( < 0.001), but not in patients with seizures with PCCA ( = 0.22). Postictal minus interictal 5-HT levels also differed between the 2 groups with and without PCCA ( = 0.03). Increased heart rate was accompanied by increased serum 5-HT levels (postictal minus interictal) after seizures without PCCA ( = 0.03) compared to those with PCCA ( = 0.42).

Conclusions: The data suggest that significant seizure-related increases in serum 5-HT levels are associated with a lower incidence of seizure-related breathing dysfunction, and may reflect physiologic changes that confer a protective effect against deleterious phenomena leading to SUDEP. These results need to be confirmed with a larger sample size study.
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http://dx.doi.org/10.1212/WNL.0000000000008244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010327PMC
October 2019

Insular resection may lead to autonomic function changes.

Epilepsy Behav 2019 10 30;99:106475. Epub 2019 Aug 30.

Epilepsy Center, UT Health Sciences Center Houston, TX, USA; NINDS Center for SUDEP Research (CSR), USA.

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http://dx.doi.org/10.1016/j.yebeh.2019.106475DOI Listing
October 2019

Ictal Central Apnea (ICA) may be a useful semiological sign in invasive epilepsy surgery evaluations.

Epilepsy Res 2019 10 10;156:106164. Epub 2019 Jul 10.

Epilepsy Center, UT Houston Health Sciences Center, TX, USA.

Introduction: Ictal central apnea (ICA) occurs in up to 44% focal seizures (temporal > extratemporal) and precedes scalp electrographic (EEG) seizure onset in 54% of them. Central apnea can be elicited by electrical stimulation of mesial temporal structures (amygdala, hippocampus, and anteromesial parahippocampal and fusiform gyri), known symptomatogenic anatomical substrates for ICA. We aimed to analyze ICA value as an early semiological sign in invasive evaluation of suspected mesial temporal lobe epilepsy (MTLE).

Methods: We examined seizure records of intractable, suspected MTLE patients undergoing intracranial EEG (ICEEG) evaluations who had simultaneous respiratory belts with artifact-free signal.

Results: We analyzed 32 seizures (11 patients). ICA was seen in 22/32 (68.7%) seizures in 9 patients, was the first clinical manifestation in all of them, and the only clinical sign in 5/32 (15.6%). ICA onset occurred simultaneously or after ICEEG seizure onset in 20/22 (91%) seizures by 4.9 +4.6 [0-14] seconds. In one patient with bilateral amygdalar and hippocampal implantation, ICA occurred before ICEEG seizure onset, indicating seizure discharge in an untargeted, probably extra amygdalohippocampal, symptomatogenic location.

Conclusions: ICA incidence in mesial temporal lobe (MTL) seizures is 68.7%. ICA is often the first clinical sign and sometimes the only clinical manifestation in MTLE, but usually goes unrecognized. ICA recognition may help anatomo-electro-clinical localization of clinical seizure onset to known symptomatogenic areas. ICA preceding ICEEG onset may indicate inadequate putative epileptogenic zone coverage, and may impact surgical outcomes. Respiratory monitoring in surgical evaluations is of critical importance and should be carried out as standard of care.
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http://dx.doi.org/10.1016/j.eplepsyres.2019.106164DOI Listing
October 2019

Barriers to epilepsy care in Central Uganda, a qualitative interview and focus group study involving PLWE and their caregivers.

BMC Neurol 2019 Jul 17;19(1):161. Epub 2019 Jul 17.

Neurological and Behavioral Outcomes Center, University Hospitals Cleveland Medical Center & Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.

Background: Epilepsy is a common neurological disease with substantial impact on the subject and their caretakers. This exploratory study identified barriers to care for persons living with epilepsy (PLWE) to develop a culturally acceptable nurse led self-management intervention for PLWE previously developed in the United States.

Methods: The study involving 48 participants (31 PLWE and 17 caregivers) with in depth interviews and focus groups was conducted. We obtained insights into barriers to care in PLWE and their caregivers. Using a thematic analytic procedure emphasizing the dominant themes the qualitative responses were analyzed. Median age of PLWE was 24 years (IQR 19-30), and10 (52.6%) were male. The median age of epilepsy onset was 12 years (IQR 6-18), range of 1-37 years. The median age of caregivers was 50 years (IQR 45-50.5), with a range of 18-78 years. Seventy five percent of caregivers (6/8) were females.

Results: Three major areas of perceived barriers involving individual, family or community and provider and healthcare system barriers to epilepsy care emerged. Individual factors like limited epilepsy knowledge and medication non-adherence were reported to be key barriers to epilepsy care. Caregiver burden and lack of family support as well as poor health care access were identified from the family and health care systems.

Conclusions: The main barrier to epilepsy care is limited epilepsy knowledge in PLWE and their caregivers. Improving epilepsy care awareness and knowledge within communities and appropriate health care provider service for epilepsy would help reduce epilepsy barriers and improve care.
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http://dx.doi.org/10.1186/s12883-019-1398-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635990PMC
July 2019