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  1. Artificial neural network based epileptic detection using time-domain and frequency-domain features
    Srinivasan V, Eswaran C, Sriraam N
    J Med Syst, 2005 Dec;29(6):647-60.
    PMID: 16235818
    Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.
    Matched MeSH terms: Seizures/physiopathology
  2. Fulltext Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques
    Sahu R, Dash SR, Cacha LA, Poznanski RR, Parida S
    J Integr Neurosci, 2020 Mar 30;19(1):1-9.
    PMID: 32259881 DOI: 10.31083/j.jin.2020.01.24
    Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold cross-validation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.
    Matched MeSH terms: Seizures/physiopathology
  3. Fulltext A signal processing based analysis and prediction of seizure onset in patients with epilepsy
    Namazi H, Kulish VV, Hussaini J, Hussaini J, Delaviz A, Delaviz F, et al.
    Oncotarget, 2016 Jan 5;7(1):342-50.
    PMID: 26586477 DOI: 10.18632/oncotarget.6341
    One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence.
    Matched MeSH terms: Seizures/physiopathology*
  4. Newborn seizure detection based on heart rate variability
    Malarvili MB, Mesbah M
    IEEE Trans Biomed Eng, 2009 Nov;56(11):2594-603.
    PMID: 19628449 DOI: 10.1109/TBME.2009.2026908
    In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of time-frequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7% sensitivity and 84.6% specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.
    Matched MeSH terms: Seizures/physiopathology
  5. Periodic lateralized epileptiform discharges in neuropsychiatric lupus: association with cerebritis in magnetic resonance imaging and resolution after intravenous immunoglobulin
    Lim KS, Cheong KL, Tan CT
    Lupus, 2010 May;19(6):748-52.
    PMID: 20133346 DOI: 10.1177/0961203309351539
    A 13-year-old girl with a known diagnosis of systemic lupus erythematosus presented with seizures and psychosis. An electroencephalogram (EEG) revealed continuous, non-evolving periodic lateralized epileptiform discharges (PLEDs) in the left temporal region, which did not resolve with benzodiazepine. A magnetic resonance imaging (MRI) brain scan demonstrated a focal hyperintensity in the left medial temporal and left occipital lobes, left thalamus and bilateral cerebellar white matter, with evidence of vasculitis in the magnetic resonance angiography. Intravenous immunoglobulin was given because of failed steroid therapy, which resulted in a full resolution of clinical, EEG and MRI abnormalities. Lupus cerebritis should be considered as a possible aetiology in PLEDs, and immunoglobulin can be effective in neuropsychiatric lupus.
    Matched MeSH terms: Seizures/physiopathology
  6. Effect of fluoxetine on maximal electroshock seizures in mice: acute vs chronic administration
    Raju SS, Noor AR, Gurthu S, Giriyappanavar CR, Acharya SB, Low HC, et al.
    Pharmacol Res, 1999 Jun;39(6):451-4.
    PMID: 10373242
    There are no definite reports regarding the effects of chronic fluoxetine on animal models of epilepsy. Since chronically administered fluoxetine, in comparison to acutely administered fluoxetine has different effects on CNS, the present study was undertaken to investigate the effect of acute and chronic fluoxetine pretreatment, on a median anticonvulsant dose (ED50) of phenytoin in male ICR albino mice. Additionally, the effects of fluoxetine pretreatment on median convulsive current (CC50) in the presence and absence of phenytoin were investigated and results were compared. The maximal electroshock seizure (MES) test was used to estimate the ED50of phenytoin. The electroshock threshold test was used to estimate CC50. ED50and CC50values were calculated by probit analysis. The effects of the chronic and acute fluoxetine groups on the ED50of phenytoin were significantly different (P<0.05), and on CC50this difference was not statistically significant. Chronic fluoxetine insignificantly increased the ED50of phenytoin and decreased the CC50while acute fluoxetine decreased the ED50of phenytoin and increased the CC50. Our results indicate that chronic fluoxetine does not have an antiepileptic property and it may have dubious proconvulsant properties, contrary to acute fluoxetine.
    Matched MeSH terms: Seizures/physiopathology
  7. A novel homozygous truncating variant of NECAP1 in early infantile epileptic encephalopathy: the second case report of EIEE21
    Mizuguchi T, Nakashima M, Moey LH, Ch'ng GS, Khoo TB, Mitsuhashi S, et al.
    J Hum Genet, 2019 Apr;64(4):347-350.
    PMID: 30626896 DOI: 10.1038/s10038-018-0556-2
    We report the second case of early infantile epileptic encephalopathy (EIEE) arising from a homozygous truncating variant of NECAP1. The boy developed infantile-onset tonic-clonic and tonic seizures, then spasms in clusters. His electroencephalogram (EEG) showed a burst suppression pattern, leading to the diagnosis of Ohtahara syndrome. Whole-exome sequencing revealed the canonical splice-site variant (c.301 + 1 G > A) in NECAP1. In rodents, Necap1 protein is enriched in neuronal clathrin-coated vesicles and modulates synaptic vesicle recycling. cDNA analysis confirmed abnormal splicing that produced early truncating mRNA. There has been only one previous report of a mutation in NECAP1 in a family with EIEE; this was a nonsense mutation (p.R48*) that was cited as EIEE21. Decreased mRNA levels and the loss of the WXXF motif in both the families suggests that loss of NECAP1 function is a common pathomechanism for EIEE21. This study provided additional support that synaptic vesicle recycling plays a key role in epileptogenesis.
    Matched MeSH terms: Seizures/physiopathology
  8. Fulltext Impaired neuronal KCC2 function by biallelic SLC12A5 mutations in migrating focal seizures and severe developmental delay
    Saitsu H, Watanabe M, Akita T, Ohba C, Sugai K, Ong WP, et al.
    Sci Rep, 2016 07 20;6:30072.
    PMID: 27436767 DOI: 10.1038/srep30072
    Epilepsy of infancy with migrating focal seizures (EIMFS) is one of the early-onset epileptic syndromes characterized by migrating polymorphous focal seizures. Whole exome sequencing (WES) in ten sporadic and one familial case of EIMFS revealed compound heterozygous SLC12A5 (encoding the neuronal K(+)-Cl(-) co-transporter KCC2) mutations in two families: c.279 + 1G > C causing skipping of exon 3 in the transcript (p.E50_Q93del) and c.572 C >T (p.A191V) in individuals 1 and 2, and c.967T > C (p.S323P) and c.1243 A > G (p.M415V) in individual 3. Another patient (individual 4) with migrating multifocal seizures and compound heterozygous mutations [c.953G > C (p.W318S) and c.2242_2244del (p.S748del)] was identified by searching WES data from 526 patients and SLC12A5-targeted resequencing data from 141 patients with infantile epilepsy. Gramicidin-perforated patch-clamp analysis demonstrated strongly suppressed Cl(-) extrusion function of E50_Q93del and M415V mutants, with mildly impaired function of A191V and S323P mutants. Cell surface expression levels of these KCC2 mutants were similar to wildtype KCC2. Heterologous expression of two KCC2 mutants, mimicking the patient status, produced a significantly greater intracellular Cl(-) level than with wildtype KCC2, but less than without KCC2. These data clearly demonstrated that partially disrupted neuronal Cl(-) extrusion, mediated by two types of differentially impaired KCC2 mutant in an individual, causes EIMFS.
    Matched MeSH terms: Seizures/physiopathology*
  9. Automated seizure prediction
    Acharya UR, Hagiwara Y, Adeli H
    Epilepsy Behav, 2018 11;88:251-261.
    PMID: 30317059 DOI: 10.1016/j.yebeh.2018.09.030
    In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a high degree of accuracy. In recent years, the frontiers of computational epilepsy research have moved to seizure prediction, a more challenging problem. While antiepileptic medication can result in complete seizure freedom in many patients with epilepsy, up to one-third of patients living with epilepsy will have medically intractable epilepsy, where medications reduce seizure frequency but do not completely control seizures. If a seizure can be predicted prior to its clinical manifestation, then there is potential for abortive treatment to be given, either self-administered or via an implanted device administering medication or electrical stimulation. This will have a far-reaching impact on the treatment of epilepsy and patient's quality of life. This paper presents a state-of-the-art review of recent efforts and journal articles on seizure prediction. The technologies developed for epilepsy diagnosis and seizure detection are being adapted and extended for seizure prediction. The paper ends with some novel ideas for seizure prediction using the increasingly ubiquitous machine learning technology, particularly deep neural network machine learning.
    Matched MeSH terms: Seizures/physiopathology
  10. Rapidly progressive dementia with seizures: a post-dengue complication
    Mohammed AP, Koraddi A, Prabhu A, Kotian CM, Umakanth S
    Trop Doct, 2020 Jan;50(1):81-83.
    PMID: 31694475 DOI: 10.1177/0049475519885798
    Dengue infection can cause various effects on the central and peripheral nervous systems. Direct neurotropism and immunological mechanisms are responsible for most such neurological manifestations. We present the case of a 64-year-old woman with rapidly progressive dementia with seizures following dengue infection.
    Matched MeSH terms: Seizures/physiopathology
  11. Fulltext Clinical characteristics and risk factors for a complex first febrile convulsion
    Ling SG
    Singapore Med J, 2001 Jun;42(6):264-7.
    PMID: 11547964
    To determine the frequency of complex features in febrile convulsion, association of complex febrile convulsion with neurological findings and risk factors associated with complex febrile convulsion.
    Matched MeSH terms: Seizures/physiopathology
  12. Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
    Al-Qazzaz NK, Alrahhal M, Jaafer SH, Ali SHBM, Ahmad SA
    Med Eng Phys, 2024 Aug;130:104206.
    PMID: 39160030 DOI: 10.1016/j.medengphy.2024.104206
    Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All-time-entropy fusion feature improves the final classification results.
    Matched MeSH terms: Seizures/physiopathology
  13. Jeavons syndrome in China
    Wang XL, Bao JX, Liang-Shi, Tie-Ma, Deng YC, Zhao G, et al.
    Epilepsy Behav, 2014 Mar;32:64-71.
    PMID: 24495864 DOI: 10.1016/j.yebeh.2013.12.016
    Jeavons syndrome (JS) is one of the underreported epileptic syndromes and is characterized by eyelid myoclonia (EM), eye closure-induced seizures or electroencephalography (EEG) paroxysms, and photosensitivity. In the Western populations, it has been reported to be characterized by focal posterior, occipital predominant epileptiform discharges (OPEDs) or frontal predominant epileptiform discharges (FPEDs) followed by generalized EDs in both interictal and ictal EEG recordings. However, it is not clear if there are different clinical manifestations between OPEDs and FPEDs. The clinical and electrographic presentations in the Chinese population are largely unknown. Here, we report the clinical and electroencephalographic features of 50 Chinese patients with JS and evaluate for the presence of different clinical features between patients with OPEDs and patients with FPEDs.
    Matched MeSH terms: Seizures/physiopathology
  14. Identification of de novo CSNK2A1 and CSNK2B variants in cases of global developmental delay with seizures
    Nakashima M, Tohyama J, Nakagawa E, Watanabe Y, Siew CG, Kwong CS, et al.
    J Hum Genet, 2019 Apr;64(4):313-322.
    PMID: 30655572 DOI: 10.1038/s10038-018-0559-z
    Casein kinase 2 (CK2) is a serine threonine kinase ubiquitously expressed in eukaryotic cells and involved in various cellular processes. In recent studies, de novo variants in CSNK2A1 and CSNK2B, which encode the subunits of CK2, have been identified in individuals with intellectual disability syndrome. In this study, we describe four patients with neurodevelopmental disorders possessing de novo variants in CSNK2A1 or CSNK2B. Using whole-exome sequencing, we detected two de novo variants in CSNK2A1 in two unrelated Japanese patients, a novel variant c.571C>T, p.(Arg191*) and a recurrent variant c.593A>G, p.(Lys198Arg), and two novel de novo variants in CSNK2B in Japanese and Malaysian patients, c.494A>G, p.(His165Arg) and c.533_534insGT, p.(Pro179Tyrfs*49), respectively. All four patients showed mild to profound intellectual disabilities, developmental delays, and various types of seizures. This and previous studies have found a total of 20 CSNK2A1 variants in 28 individuals with syndromic intellectual disability. The hotspot variant c.593A>G, p.(Lys198Arg) was found in eight of 28 patients. Meanwhile, only five CSNK2B variants were identified in five individuals with neurodevelopmental disorders. We reviewed the previous literature to verify the phenotypic spectrum of CSNK2A1- and CSNK2B-related syndromes.
    Matched MeSH terms: Seizures/physiopathology
  15. 48-hour video-EEG monitoring for epilepsy presurgical evaluation is cost-effective and safe in resource-limited setting
    Lim KS, Fong SL, Thuy Le MA, Ahmad Bazir S, Narayanan V, Ismail N, et al.
    Epilepsy Res, 2020 05;162:106298.
    PMID: 32172144 DOI: 10.1016/j.eplepsyres.2020.106298
    INTRODUCTION: Video-EEG monitoring is one of the key investigations in epilepsy pre-surgical evaluation but limited by cost. This study aimed to determine the efficacy and safety of a 48-hour (3-day) video EEG monitoring, with rapid pre-monitoring antiepileptic drugs withdrawal.

    MATERIAL AND METHODS: This is a retrospective study of epilepsy cases with VEM performed in University Malaya Medical Center (UMMC), Kuala Lumpur, from January 2012 till August 2016.

    RESULTS: A total of 137 cases were included. The mean age was 34.5 years old (range 15-62) and 76 (55.8 %) were male. On the first 24 -h of recording (D1), 81 cases (59.1 %) had seizure occurrence, and 109 (79.6 %) by day 2 (D2). One-hundred and nine VEMs (79.6 %) were diagnostic, in guiding surgical decision or further investigations. Of these, 21 had less than 2 seizures recorded in the first 48 h but were considered as diagnostic because of concordant interictal ± ictal activities, or a diagnosis such as psychogenic non-epileptic seizure was made. Twenty-eight patients had extension of VEM for another 24-48 h, and 11 developed seizures during the extension period. Extra-temporal lobe epilepsy and seizure frequency were significant predictors for diagnostic 48 -h VEM. Three patients developed complications, including status epilepticus required anaesthetic agents (1), seizure clusters (2) with postictal psychosis or dysphasia, and all recovered subsequently.

    CONCLUSIONS: 48-h video EEG monitoring is cost-effective in resource limited setting.

    Matched MeSH terms: Seizures/physiopathology
  16. Refractory seizures in a young army cohort
    Loh NK, Lee WL, Yew WW, Tjia TL
    Ann Acad Med Singap, 1997 Jul;26(4):471-4.
    PMID: 9395813
    This survey covered male Singapore citizens born in 1974 who were medically screened at the age of 18 years before enlistment for compulsory military service. Suspected epileptics were referred to government hospitals for further management. Out of 20,542 men, there were 121 epileptics, giving a cumulative incidence of 5 per 1000 by age 18 years. We had information on 106 (87%) of these individuals and were able to interview them and review their hospital records. Seventy-three of the 106 (69%) epileptics had generalised seizures while 14 (13%) had refractory seizures. There was no statistically significant racial bias amongst these epileptics. Unprovoked afebrile seizures occurred early in these patients, half of whom had seizures onset before 7 years of age. Nine refractory epileptics had a history of febrile seizures, 4 of which were complex febrile seizures. Magnetic resonance imaging identified mesial temporal sclerosis in 2 patients and a hypothalamic lesion in 1 patient. Computed tomographic scans revealed focal cortical atrophy in 2 patients. Nine other patients had normal imaging studies. Nine out of 14 (64%) patients with refractory epilepsy had partial seizures; 4 (29%) had generalised seizures and 1 (7%) was unclassified. This is in contrast to the distribution of the entire cohort of epileptics studied. Two out of 9 patients with refractory partial seizures (gelastic epilepsy and mesial temporal sclerosis) had undergone surgery while 6 of the other 7 patients refused to consider surgery.
    Matched MeSH terms: Seizures/physiopathology
  17. Cardiorespiratory findings in epilepsy: A recent review on outcomes and pathophysiology
    Akyüz E, Üner AK, Köklü B, Arulsamy A, Shaikh MF
    J Neurosci Res, 2021 09;99(9):2059-2073.
    PMID: 34109651 DOI: 10.1002/jnr.24861
    Epilepsy is a debilitating disorder of uncontrollable recurrent seizures that occurs as a result of imbalances in the brain excitatory and inhibitory neuronal signals, that could stem from a range of functional and structural neuronal impairments. Globally, nearly 70 million people are negatively impacted by epilepsy and its comorbidities. One such comorbidity is the effect epilepsy has on the autonomic nervous system (ANS), which plays a role in the control of blood circulation, respiration and gastrointestinal function. These epilepsy-induced impairments in the circulatory and respiratory systems may contribute toward sudden unexpected death in epilepsy (SUDEP). Although, various hypotheses have been proposed regarding the role of epilepsy on ANS, the linking pathological mechanism still remains unclear. Channelopathies and seizure-induced damages in ANS-control brain structures were some of the causal/pathological candidates of cardiorespiratory comorbidities in epilepsy patients, especially in those who were drug resistant. However, emerging preclinical research suggest that neurotransmitter/receptor dysfunction and synaptic changes in the ANS may also contribute to the epilepsy-related autonomic disorders. Thus, pathological mechanisms of cardiorespiratory dysfunction should be elucidated by considering the modifications in anatomy and physiology of the autonomic system caused by seizures. In this regard, we present a comprehensive review of the current literature, both clinical and preclinical animal studies, on the cardiorespiratory findings in epilepsy and elucidate the possible pathological mechanisms of these findings, in hopes to prevent SUDEP especially in patients who are drug resistant.
    Matched MeSH terms: Seizures/physiopathology
  18. Development of the clinical assessment scale in autoimmune encephalitis
    Lim JA, Lee ST, Moon J, Jun JS, Kim TJ, Shin YW, et al.
    Ann Neurol, 2019 03;85(3):352-358.
    PMID: 30675918 DOI: 10.1002/ana.25421
    OBJECTIVE: There is no scale for rating the severity of autoimmune encephalitis (AE). In this study, we aimed to develop a novel scale for rating severity in patients with diverse AE syndromes and to verify the reliability and validity of the developed scale.

    METHODS: The key items were generated by a panel of experts and selected according to content validity ratios. The developed scale was initially applied to 50 patients with AE (development cohort) to evaluate its acceptability, reproducibility, internal consistency, and construct validity. Then, the scale was applied to another independent cohort (validation cohort, n = 38).

    RESULTS: A new scale consisting of 9 items (seizure, memory dysfunction, psychiatric symptoms, consciousness, language problems, dyskinesia/dystonia, gait instability and ataxia, brainstem dysfunction, and weakness) was developed. Each item was assigned a value of up to 3 points. The total score could therefore range from 0 to 27. We named the scale the Clinical Assessment Scale in Autoimmune Encephalitis (CASE). The new scale showed excellent interobserver (intraclass correlation coefficient [ICC] = 0.97) and intraobserver (ICC = 0.96) reliability for total scores, was highly correlated with modified Rankin scale (r = 0.86, p

    Matched MeSH terms: Seizures/physiopathology
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