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Alva‐Diaz C, Cabanillas‐Lazo M, Navarro‐Flores A, Martinez‐Rivera RN, Valdeiglesias‐Abarca M, Acevedo‐Marino K, Pacheco‐Barrios K, Ruiz‐Garcia R, Burneo J. Peri-ictal psychiatric manifestations in people with epilepsy: An umbrella review. Epilepsia Open 2024; 9:1166-1175. [PMID: 38816942 PMCID: PMC11296096 DOI: 10.1002/epi4.12949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 04/02/2024] [Accepted: 04/10/2024] [Indexed: 06/01/2024] Open
Abstract
OBJECTIVE We aimed to conduct an umbrella review to summarize the existing evidence regarding the prevalence of peri-ictal psychiatric manifestations (PM) in people with epilepsy (PWE) including pre-ictal, ictal, and postictal stages. METHODS Databases were searched up to June 2023 for systematic reviews (SR) of observational studies that included patients with epilepsy peri-ictal PM. Data selection, data extraction, and risk of bias assessment (with the AMSTAR-2 instrument) were performed by two independent reviewers. We performed a narrative synthesis using previous guidelines. We used a self-developed decision table according to the GRADE system adapted for narrative outcomes if the certainty of outcomes was not determined by systematic review authors. RESULTS Four SRs were included comprising 66 primary studies (n = 10 217). Three SRs evaluated one period (pre-ictal, ictal, and postictal), and one did not determine it. During the pre-ictal period, the more prevalent symptom was confusion, although with a low certainty (due to the heterogeneity and serious risk of bias). One systematic review that only included case reports evaluated the ictal period, finding mood/anxiety disorders, psychosis, and personality changes. The postictal period included the most PM (anxiety: 45.0% and depressive symptoms: 43.0%), with very low certainty, due to risk of bias, potential publication bias, heterogeneity, and failure to report the confidence intervals. SIGNIFICANCE With very low certainty, epileptic periods are characterized by a wide spectrum of PM, being postictal symptoms the most prevalent, predominantly anxiety, and depressive symptoms. Further understanding of these PM of epilepsy could improve the attention of the people with epilepsy. PLAIN LANGUAGE SUMMARY In this review of reviews, we summarize the frequency in which psychiatric manifestations occur in relation to an epileptic seizure. A total of 10 217 patients were reported in the reviews. The most common manifestations included symptoms of anxiety and depression, as well as changes in the normal behavior of the patient. These manifestations occurred most frequently right after the seizure finished.
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Affiliation(s)
- Carlos Alva‐Diaz
- Grupo de Investigación Neurociencia, Efectividad Clínica y Salud PúblicaUniversidad Científica del SurLimaPeru
- Servicio de Neurología, Departamento de Medicina y Oficina de Apoyo a la Docencia e Investigación (OADI)Hospital Daniel Alcides CarriónCallaoPeru
| | | | - Alba Navarro‐Flores
- International Max Planck Research School for Translational Psychiatry (IMPRS‐TP)MunichGermany
| | | | - Maria Valdeiglesias‐Abarca
- Servicio de Neurología, Departamento de Medicina y Oficina de Apoyo a la Docencia e Investigación (OADI)Hospital Daniel Alcides CarriónCallaoPeru
- Universidad Ricardo PalmaLimaPeru
| | - Krystel Acevedo‐Marino
- Servicio de Neurología, Departamento de Medicina y Oficina de Apoyo a la Docencia e Investigación (OADI)Hospital Daniel Alcides CarriónCallaoPeru
- Universidad Ricardo PalmaLimaPeru
| | - Kevin Pacheco‐Barrios
- Neuromodulation Center and Center for Clinical Research LearningSpaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Vicerrectorado de Investigación, Unidad de Investigación Para la Generación y Síntesis de Evidencias en SaludUniversidad San Ignacio de LoyolaLimaPeru
| | - Ramiro Ruiz‐Garcia
- Department of NeuropsychiatryNational Institute of Neurology and NeurosurgeryMexico CityMexico
| | - Jorge Burneo
- Epilepsy Program and Neuroepidemiology Unit, Department of Clinical Neurological Sciences, Schulich School of MedicineWestern UniversityLondonOntarioCanada
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Samsonsen C, Mestvedthagen G, Uglem M, Brodtkorb E. Disentangling the cascade of seizure precipitants: A prospective observational study. Epilepsy Behav 2023; 145:109339. [PMID: 37413785 DOI: 10.1016/j.yebeh.2023.109339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND The management of epilepsy includes appropriate antiseizure medication (ASM) treatment and careful avoidance of seizure precipitating factors. Seizure precipitants may be multiple occurring with low intensity adding to each other, thus leaving essential elements unrecognized. The aim of this study was to reveal the patients' subjective perceptions of the most important factors and to compare them with standardized measurements. METHODS The study included 152 acute hospital admissions for seizures. The patients were asked to score the impact of various seizure precipitants as perceived by themselves on a visual analogue scale (VAS). The following items related to seizure occurrence were quantified: sleep deprivation by sleep diaries, ASM adherence by therapeutic drug monitoring, the Alcohol Use Identification Test, and the Hospital Anxiety and Depression Scale. Statistical analyses, including multiple regression, were performed to discover relationships between various parameters. RESULTS The interaction of the various factors was high. The association between lack of sleep and hazardous drinking and anxiety was highly significant. Perceived stress correlated well with anxiety and depression. Relatively low VAS scores for missed medication in patients with identified non-adherence suggest that insufficient patient awareness is common. Low VAS-scores for alcohol in patients with harmful drinking also suggest low acknowledgment of alcohol-related seizures. High alcohol scores were associated with sleep deprivation, anxiety and depression. CONCLUSION The circumstances leading to an epileptic seizure are complex. Stress, sleep loss, alcohol intake, and missed medication are among the most commonly reported seizure precipitants. They are often combined, and various facets of the same underlying cause may be at play. Their sequence and relative impact are often difficult to establish. Improved understanding of the cascade of events preceding a seizure can improve comprehensive personalized management of uncontrolled epilepsy.
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Affiliation(s)
- Christian Samsonsen
- Department of Neurology and Clinical Neurophysiology, St.Olav University Hospital, Trondheim, Norway; Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Guro Mestvedthagen
- Faculty of Medicine and Health Sciences Norwegian University of Science and Technology, Trondheim, Norway.
| | - Martin Uglem
- Department of Neurology and Clinical Neurophysiology, St.Olav University Hospital, Trondheim, Norway; Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Eylert Brodtkorb
- Department of Neurology and Clinical Neurophysiology, St.Olav University Hospital, Trondheim, Norway; Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.
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Abstract
OBJECTIVE Uncontrolled epilepsy creates a constant source of worry for patients and puts them at a high risk of injury. Identifying recurrent "premonitory" symptoms of seizures and using them to recalibrate seizure prediction algorithms may improve prediction performances. This study aimed to investigate patients' ability to predict oncoming seizures based on preictal symptoms. METHODS Through an online survey, demographics and clinical characteristics (e.g., seizure frequency, epilepsy duration, and postictal symptom duration) were collected from people with epilepsy and caregivers across Canada. Respondents were asked to answer questions regarding their ability to predict seizures through warning symptoms. A total of 196 patients and 150 caregivers were included and were separated into three groups: those who reported warning symptoms within the 5 minutes preceding a seizure, prodromes (symptoms earlier than 5 minutes before seizure), and no warning symptoms. RESULTS Overall, 12.2% of patients and 12.0% of caregivers reported predictive prodromes ranging from 5 minutes to more than 24 hours before the seizures (median of 2 hours). The most common were dizziness/vertigo (28%), mood changes (26%), and cognitive changes (21%). Statistical testing showed that respondents who reported prodromes also reported significantly longer postictal recovery periods compared to those who did not report predictive prodromes (P < 0.05). CONCLUSION Findings suggest that patients who present predictive seizure prodromes may be characterized by longer patient-reported postictal recovery periods. Studying the correlation between seizure severity and predictability and investigating the electrical activity underlying prodromes may improve our understanding of preictal mechanisms and ability to predict seizures.
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Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120781. [PMID: 36550987 PMCID: PMC9774931 DOI: 10.3390/bioengineering9120781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/13/2022]
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
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Angus-Leppan H. Migraine in people with epilepsy: a treatable and neglected co-morbidity. ADVANCES IN CLINICAL NEUROSCIENCE & REHABILITATION 2022. [DOI: 10.47795/ishy1373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Migraine and epilepsy account for more than 40% of neurology outpatients and are leading causes of disability. They often co-exist and can be confused, because of shared clinical features. The borderlands and links between migraine and epilepsy have fascinated neurologists for centuries, and unresolved questions remain. Greater understanding of the relationship between migraine and epilepsy may give insight into shared mechanisms. It is already clear that treating co-existing migraine is an important therapeutic opportunity and may improve epilepsy.
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Huang S, Chen R, Chen H, Si G. Abnormal electroencephalogram (EEG) after drug withdrawal is a risk factor for epilepsy recurrence in children: a systematic review and meta-analysis. Transl Pediatr 2022; 11:947-953. [PMID: 35800270 PMCID: PMC9253940 DOI: 10.21037/tp-22-206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/01/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The relationship between abnormal electroencephalogram (EEG) and epilepsy recurrence after antiepileptic drug (AED) withdrawal has been controversial. We aimed to explore the relationship between abnormal EEG after AED withdrawal and the risk of epilepsy recurrence in children. METHODS Literature retrieval was performed using the PubMed, EMBASE, Medline, CENTRAL, and China National Knowledge Infrastructure (CNKI) databases. Included literatures were subjects of pediatric epilepsy patients who discontinued medication. The recurrence rate of epilepsy in patients with normal and abnormal EEG after AED withdrawal was observed. The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of literatures. The Chi-square test was used to test heterogeneity. If heterogeneity between the articles existed, a random-effects model was used; otherwise, fixed-effects models were used. Subgroup analysis was used to explore the causes of heterogeneity. The odds ratio (OR) and 95% confidence interval (CI) were calculated using the Mantel-Haenszel statistical method. OR was not adjusted for other factors. RESULTS A total of 843 articles were retrieved. Nine studies were included, with a total of 1,663 patients, including 1,299 patients with normal EEG and 364 patients with abnormal EEG. Compared with the normal EEG patients, the OR of recurrence rate after AEDs withdrawal was 3.02 (P=0.0003), with heterogeneity (P<0.0001). The funnel plot indicated that there was no publication bias among the studies. The not partial seizure group analysis showed OR =1.70 (P=0.003) and no heterogeneity (P=0.70) in patients with abnormal EEG compared to those with normal EEG. In the partial seizures subgroup, the OR of the recurrence rate after AED withdrawal was 8.08 (P<0.00001) compared with the normal EEG patients, and there was no heterogeneity (P=0.29). The funnel chart shows that the partial seizures type subgroup analysis revealed positive results, while the not partial seizure group analysis reported negative results, indicating publication bias. CONCLUSIONS The risk of epilepsy recurrence is higher in children with abnormal EEG after AED withdrawal, regardless of seizure type. For pediatric epilepsy patients with abnormal EEG after AED withdrawal, a more cautious discontinuation regimen, closer follow-up and monitoring are required.
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Affiliation(s)
- Shanwen Huang
- Department of Pediatrics, Haikou Maternal and Child Health Hospital, Haikou, China
| | - Ruipeng Chen
- Department of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Hao Chen
- Department of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gang Si
- Department of Pharmacy, Haikou Maternal and Child Health Hospital, Haikou, China
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Zawadzka M, Anuszkiewicz K, Szmuda M, Błaszczyk W, Knurowska A, Stogowski P, Sokolewicz EM, Waszak P, Mazurkiewicz-Bełdzińska M. Epilepsy awareness among school-aged students in Poland. Epilepsy Behav 2022; 128:108603. [PMID: 35151191 DOI: 10.1016/j.yebeh.2022.108603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/20/2022] [Accepted: 01/25/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Epilepsy can be a well-controlled condition with only a slight impact on patients' life. Lack of knowledge within society contributes to children with epilepsy experiencing discrimination and hostility. The aim of this study was to evaluate the awareness of epilepsy and general views on people struggling with this disease among school-aged children. METHODS The study was conducted on a random sample of Polish school students, in total 472 participants. Participants' knowledge was assessed by a self-completed survey. RESULTS Students are unaware of the wide range of symptoms occurring during seizures. More than half claimed that people experiencing epilepsy should not perform sports activities. Alarmingly, 30% of participants believe that those patients should not leave the house and they should be excluded from many jobs. Almost all participants would help a person experiencing seizures and remember proper head protection; shockingly, 20% of children would try to put something in the person's mouth. Older students seem to be better educated on epilepsy, but the percentage of incorrect personal beliefs and myths is similar for each age group. SIGNIFICANCE School-aged students have insufficient knowledge of epilepsy. More emphasis should be put on first aid during seizures and on the spectrum of epilepsy symptoms. Educating society is important for life quality of people experiencing epilepsy.
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Affiliation(s)
- Marta Zawadzka
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland.
| | - Karolina Anuszkiewicz
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland
| | - Marta Szmuda
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland
| | - Weronika Błaszczyk
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland
| | - Agata Knurowska
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland
| | - Piotr Stogowski
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland
| | - Ewa Maria Sokolewicz
- Department of Developmental Neurology, Medical University of Gdańsk, Dębinki 7 Street, 80-952 Gdańsk, Poland
| | - Przemysław Waszak
- Department of Hygiene and Epidemiology, Department of Developmental Psychiatry, Psychotic and Geriatric Disorders, Dębinki 7 Street, 80-952 Gdańsk, Poland
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Park S, Lee H, Kim JH, Jeon HL, Shin JY. Association between tramadol use and seizures: A nationwide case-case-time-control study. Pharmacoepidemiol Drug Saf 2022; 31:614-622. [PMID: 35141978 DOI: 10.1002/pds.5417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/11/2022] [Accepted: 02/07/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE Tramadol may lower the seizure threshold; however, there is no conclusive evidence to confirm this. This study aimed to determine whether the use of tramadol is associated with the occurrence of seizures. METHODS We conducted a case-case-time-control (CCTC) study by identifying patients who had received tramadol and seizure diagnosis in a nationwide healthcare database in South Korea between 2003 and 2015. Each case was matched for age and sex to one future case to adjust for time trends in exposure without selection bias from the use of an external control group. The use of tramadol was assessed during a risk period of 1-30 days, and two reference periods, 61-90 days and 91-120 days, preceding the first diagnosis of seizures. We calculated the adjusted odds ratio (aOR) by dividing the OR in cases (case-crossover) by the OR in future cases (control-crossover). We performed a dose-response analysis using the average daily dose. RESULTS We identified 2,523 incident cases with matched future cases (mean age, 45.4 years; 50% men). The aOR for seizure with tramadol use was 0.94 (95% confidence interval [CI], 0.98-1.43) in the CCTC analysis, with a case-crossover OR of 1.19 (0.98-1.43) and control-crossover OR of 1.27 (1.03-1.56). The dose-response analysis showed a similar trend in the main analysis: a low-dose aOR of 0.80 (0.50-1.28) and a high-dose aOR of 0.92 (0.41-2.11). CONCLUSION We could not identify a significant association between transient use of tramadol and incidence of seizures in clinical practice.
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Affiliation(s)
- Sohee Park
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - Hyesung Lee
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - Ju Hwan Kim
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - Ha-Lim Jeon
- School of Pharmacy, Jeonbuk National University, Jeonju, Jeonbuk, Republic of Korea
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea.,Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.,Department of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, Republic of Korea
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Pattern recognition of epilepsy using parallel probabilistic neural network. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02509-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Demuth S, Dinkelacker V. Toward personalized machine learning approaches in care of patients with epilepsy. Epilepsia 2021; 62:3143-3145. [PMID: 34672367 DOI: 10.1111/epi.17093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/05/2021] [Accepted: 09/27/2021] [Indexed: 12/01/2022]
Affiliation(s)
- Stanislas Demuth
- Department of Neurology, Strasbourg University Hospital, Strasbourg, France
| | - Vera Dinkelacker
- Department of Neurology, Strasbourg University Hospital, Strasbourg, France
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Cousyn L, Navarro V, Chavez M. Outliers in clinical symptoms as preictal biomarkers. Epilepsy Res 2021; 177:106774. [PMID: 34571459 DOI: 10.1016/j.eplepsyres.2021.106774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/26/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022]
Abstract
Previous findings have suggested that a preictal state might precede the epileptic seizure onset, which is the basis for seizure prediction attempts. Preictal states can be apprehended as outliers that differ from an interictal baseline and display clinical changes. We collected daily clinical scores from patients with epilepsy who underwent continuous video-EEG and assessed the ability of several outlier detection methods to identify preictal states. Results from 24 patients suggested that outlying clinical features were suggestive of preictal states and can be identified by statistical methods: AUC = 0.71, 95 % CI = [0.63 - 0.79]; PPV = 0.77, 95 % CI = [0.70 - 0.84]; FPR = 0.31, 95 % CI = [0.21 - 0.44]); and F1 score = 0.74, 95 % CI = [0.64 - 0.81]. Such algorithms could be straightforwardly implemented in a mobile device (e.g., tablet or smartphone), which would allow a longer data collection that could improve prediction performances. Additional clinical - and even multimodal - parameters could identify more subtle physiological modifications.
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Affiliation(s)
- Louis Cousyn
- Paris Brain Institute, Inserm, CNRS, Sorbonne Université, Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France.
| | - Vincent Navarro
- Paris Brain Institute, Inserm, CNRS, Sorbonne Université, Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France
| | - Mario Chavez
- CNRS UMR-7225, Pitié-Salpêtrière Hospital, Paris, France
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The epileptic experience in the works of Dostoyevsky and Machado de Assis. Epilepsy Behav 2021; 121:106205. [PMID: 30979544 DOI: 10.1016/j.yebeh.2019.03.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 03/04/2019] [Accepted: 03/06/2019] [Indexed: 11/21/2022]
Abstract
Towards the end of the 19th century, two writers who are considered the uppermost representatives of their respective national literatures, Dostoyevsky of Russia and Machado de Assis of Brazil had epilepsy, probably both temporal lobe epilepsy, but their attitudes were opposite. Dostoyevsky was as open about his diagnosis as Machado was secretive, but both included seizure experiences in their works. Two of Dostoyevsky's many epileptic characters, Prince Myshkin in The Idiot and Kirillov in Devils, report the same ecstatic aura as Dostoyevsky did privately. That Kirillov only has isolated auras probably reflects the early phase of Dostoyevsky's epilepsy. A hitherto overlooked feature, these reports with numerous reformulations and metaphors are linguistically characteristic for self-reports of epileptic auras, related to the indescribability of the experiences. In Idiot, two seizure prodromes with great artistic skill are integrated into the fictional context. Machado in his writings never talked overtly about seizures and epilepsy, but experiences of complex partial seizures can be identified in two of his novels, Brás Cubas and Quincas Borba. One depicts a complex visual illusion, the other seems precipitated by a coincidence of several ambivalent decisions with a specific memory. Quincas Borba (1891) has several features that can be understood as an homage to Dostoyevsky's Idiot (1869). Both writers share the notion that seizures can be triggered by strong emotions, and both stand out by their mastership of seamlessly integrating seizure experiences into the fictional and psychological cosmos of their novels. This article is part of the Special Issue "NEWroscience 2018".
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Fekete T, Hinrichs H, Sitt JD, Heinze HJ, Shriki O. Multiscale criticality measures as general-purpose gauges of proper brain function. Sci Rep 2021; 11:14441. [PMID: 34262121 PMCID: PMC8280148 DOI: 10.1038/s41598-021-93880-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 07/01/2021] [Indexed: 11/09/2022] Open
Abstract
The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). MsCr measures reflect the behavior of conventional criticality measures (such as the branching parameter) across temporal scale. We applied MsCr to MEG and EEG data in several telling degraded information processing scenarios. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity: MsCr fingerprints showed deviance in the four states of compromised information processing examined in this study, disorders of consciousness, mild cognitive impairment, schizophrenia and even during pre-ictal activity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.
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Affiliation(s)
- Tomer Fekete
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.
| | - Hermann Hinrichs
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Jacobo Diego Sitt
- INSERM, U 1127, Paris, France
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, Paris, France
| | - Hans-Jochen Heinze
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel
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Cousyn L, Navarro V, Chavez M. Preictal state detection using prodromal symptoms: A machine learning approach. Epilepsia 2021; 62:e42-e47. [PMID: 33465245 DOI: 10.1111/epi.16804] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/13/2020] [Accepted: 12/13/2020] [Indexed: 12/01/2022]
Abstract
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n1 = 58) and (2) an interictal group for questionnaires completed in a 24-h period without seizures (n2 = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61-.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69-.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.
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Affiliation(s)
- Louis Cousyn
- Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Public Hospital Network of Paris, Paris, France.,Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France.,Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France.,Sorbonne University, Paris, France
| | - Vincent Navarro
- Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Public Hospital Network of Paris, Paris, France.,Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France.,Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France.,Sorbonne University, Paris, France
| | - Mario Chavez
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
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15
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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities. Sci Rep 2020; 10:21921. [PMID: 33318564 PMCID: PMC7736584 DOI: 10.1038/s41598-020-78899-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/30/2020] [Indexed: 01/01/2023] Open
Abstract
Previous research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.
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16
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Rao VR, G Leguia M, Tcheng TK, Baud MO. Cues for seizure timing. Epilepsia 2020; 62 Suppl 1:S15-S31. [PMID: 32738157 DOI: 10.1111/epi.16611] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 01/22/2023]
Abstract
The cyclical organization of seizures in epilepsy has been described since antiquity. However, historical explanations for seizure cycles-based on celestial, hormonal, and environmental factors-have only recently become testable with the advent of chronic electroencephalography (cEEG) and modern statistical techniques. Here, factors purported over millennia to influence seizure timing are viewed through a contemporary lens. We discuss the emerging concept that seizures are organized over multiple timescales, each involving differential influences of external and endogenous rhythm generators. Leveraging large cEEG datasets and circular statistics appropriate for cyclical phenomena, we present new evidence for circadian (day-night), multidien (multi-day), and circannual (about-yearly) variation in seizure activity. Modulation of seizure timing by multiscale temporal variables has implications for diagnosis and therapy in clinical epilepsy. Uncovering the mechanistic basis for seizure cycles, particularly the factors that govern multidien periodicity, will be a major focus of future work.
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Affiliation(s)
- Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Marc G Leguia
- Department of Neurology, Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | | | - Maxime O Baud
- Department of Neurology, Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland.,Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
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17
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Gong C, Zhang X, Niu Y. Identification of epilepsy from intracranial EEG signals by using different neural network models. Comput Biol Chem 2020; 87:107310. [PMID: 32599460 DOI: 10.1016/j.compbiolchem.2020.107310] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 06/15/2020] [Indexed: 11/20/2022]
Abstract
In this work, a framework is provided for identifying intracranial electroencephalography (iEEG) seizures based on discrete wavelet transform (DWT) analysis of iEEG signals using forward propagation and feedback neural networks. The performance of 5 different data sets combination classifications is studied using the probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and Elman neural network (ENN). Different feature combinations serve as the input vectors of the classifiers to obtain the best outcomes. It has been found that PNN has less running time and provides better classification accuracy (CA) than ENN and LVQ classifiers for all 5 classification problems. It is worth noticing that the CA for the C-D classification task, which shows the status of pre-ictal versus post-ictal, has been greatly improved, and reached 83.13%. Hence, the epilepsy iEEG signals pattern recognition based on DWT statistical features using the PNN classifier is more suitable for forming a reliable, automatic classification system in order to assist doctors in diagnosis.
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Affiliation(s)
- Chen Gong
- School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China
| | - Xiaoxiong Zhang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Yunyun Niu
- School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China.
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18
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Finnegan SL, Volk HA, Asher L, Daley M, Packer RMA. Investigating the potential for seizure prediction in dogs with idiopathic epilepsy: owner-reported prodromal changes and seizure triggers. Vet Rec 2020; 187:152. [PMID: 32444506 DOI: 10.1136/vr.105307] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 02/14/2020] [Accepted: 04/17/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Canine idiopathic epilepsy (IE) is characterised by recurrent seizure activity, which can appear unpredictable and uncontrollable. The purpose of this study was to investigate the potential for seizure prediction in dogs by exploring owner-perceived seizure prediction abilities and identifying owner-reported prodromal changes (long-term changes in disposition that indicate forthcoming seizures) and seizure triggers (stimuli that precipitate seizures) in dogs with IE. METHODS This is an online, international, cross-sectional survey of 229 owners of dogs diagnosed with IE, meeting the International Veterinary Epilepsy Task Force tier I diagnostic criteria. RESULTS Over half (59.6 per cent) of owners believed they were able to predict an upcoming seizure in their dog, of whom nearly half (45.5 per cent) were able to do so at least 30 minutes before the seizure commenced. The most common 'seizure predictors' were preseizure behavioural changes including increased clinginess (25.4 per cent), restlessness (23.1 per cent) and fearful behaviour (19.4 per cent). Nearly two-thirds of owners reported prodromal changes (64.9 per cent), most commonly restlessness (29.2 per cent), and nearly half (43.1 per cent) reported seizure triggers, most commonly stress (39.1 per cent). CONCLUSIONS The relatively high prevalence of owner-reported prodromal changes and seizure triggers shows promise for utilising these methods to aid seizure prediction in dogs, which could open a window of time for pre-emptive, individualised drug interventions to abort impending seizure activity.
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Affiliation(s)
| | - Holger Andreas Volk
- Clinical Science and Services, Royal Veterinary College, Hertfordshire, UK.,Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Lucy Asher
- School of Natural and Environmental Science, Newcastle University, Newcastle, UK
| | - Monica Daley
- School of Biological Sciences, UC Irvine, Irvine, California, USA.,Structure and Motion Laboratory, Royal Veterinary College, Hertfordshire, UK
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19
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Bartolini E, Sander JW. Dealing with the storm: An overview of seizure precipitants and spontaneous seizure worsening in drug-resistant epilepsy. Epilepsy Behav 2019; 97:212-218. [PMID: 31254841 DOI: 10.1016/j.yebeh.2019.05.036] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 05/21/2019] [Accepted: 05/28/2019] [Indexed: 10/26/2022]
Abstract
In drug-resistant epilepsy, periods of seizure stability may alternate with abrupt worsening, with frequent seizures limiting the individual's independence and physical, social, and psychological well-being. Here, we review the literature focusing on different clinical scenarios related to seizure aggravation in people with drug-resistant epilepsy. The role of antiseizure medication (ASM) changes is examined, especially focusing on paradoxical seizure aggravation after increased treatment. The external provocative factors that unbalance the brittle equilibrium of seizure control are reviewed, distinguishing between unspecific triggering factors, specific precipitants, and 'reflex' mechanisms. The chance of intervening surgical or medical conditions, including somatic comorbidities and epilepsy surgery failure, causing increased seizures is discussed. Spontaneous exacerbation is also explored, emphasizing recent findings on subject-specific circadian and ultradian rhythms. Awareness of external precipitants and understanding the subject-specific spontaneous epilepsy course may allow individuals to modify their lifestyles. It also allows clinicians to counsel appropriately and to institute suitable medical treatment to avoid sudden loss of seizure control.
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Affiliation(s)
- Emanuele Bartolini
- USL Centro Toscana, Neurology Unit, Nuovo Ospedale Santo Stefano, via suor Niccolina Infermiera 20, 59100 Prato, Italy.
| | - Josemir W Sander
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, United Kingdom; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0RJ, United Kingdom; Stichting Epilepsie Instelligen Nederland (SEIN), Achterweg 5, Heemstede 2103 SW, the Netherlands.
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