Tertiary wavelet model based automatic epilepsy classification system

PurposeThis work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.Design/methodology/approachIn this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.FindingsFor the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.Originality/valueEpilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods

Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.

Epilepsy in Nicolaides–Baraitser Syndrome: Review of Literature and Report of 25 Patients Focusing on Treatment Aspects

Nicolaides–Baraitser syndrome (NCBRS), caused by a mutation in the SMARCA2 gene, which goes along with intellectual disability, congenital malformations, especially of face and limbs, and often difficult-to-treat epilepsy, is surveyed focusing on epilepsy and its treatment. Patients were recruited via “Network Therapy of Rare Epilepsies (NETRE)” and an international NCBRS parent support group. Inclusion criterion is NCBRS-defining SMARCA2 mutation. Clinical findings including epilepsy classification, anticonvulsive treatment, electroencephalogram (EEG) findings, and neurodevelopmental outcome were collected with an electronic questionnaire. Inclusion of 25 NCBRS patients with epilepsy in 23 of 25. Overall, 85% of the participants (17/20) reported generalized seizures, the semiology varied widely. EEG showed generalized epileptogenic abnormalities in 53% (9/17), cranial magnetic resonance imaging (cMRI) was mainly inconspicuous. The five most frequently used anticonvulsive drugs were valproic acid (VPA [12/20]), levetiracetam (LEV [12/20]), phenobarbital (PB [8/20]), topiramate (TPM [5/20]), and carbamazepine (CBZ [5/20]). LEV (9/12), PB (6/8), TPM (4/5), and VPA (9/12) reduced the seizures' frequency in more than 50%. Temporary freedom of seizures (>6 months) was reached with LEV (4/12), PB (3/8), TPM (1/5, only combined with PB and nitrazepam [NZP]), and VPA (4/12). Seizures aggravation was observed under lamotrigine (LTG [2/4]), LEV (1/12), PB (1/8), and VPA (1/12). Ketogenic diet (KD) and vagal nerve stimulation (VNS) reduced seizures' frequency in one of two each. This first worldwide retrospective analysis of anticonvulsive therapy in NCBRS helps to treat epilepsy in NCBRS that mostly shows only initial response to anticonvulsive therapy, especially with LEV and VPA, but very rarely shows complete freedom of seizures in this, rather genetic than structural epilepsy.

Phenotypic analysis of catastrophic childhood epilepsy genes: The Epilepsy Zebrafish Project

Genetic engineering techniques have contributed to the now widespread use of zebrafish to investigate gene function, but zebrafish-based human disease studies, and particularly for neurological disorders, are limited. Here we used CRISPR-Cas9 to generate 40 single-gene mutant zebrafish lines representing catastrophic childhood epilepsies. We evaluated larval phenotypes using electrophysiological, behavioral, neuro-anatomical, survival and pharmacological assays. Phenotypes with unprovoked electrographic seizure activity (i.e., epilepsy) were identified in zebrafish lines for 8 genes; ARX, EEF1A, GABRB3, GRIN1, PNPO, SCN1A, STRADA and STXBP1. A unifying epilepsy classification scheme was developed based on local field potential recordings and blinded scoring from ~3300 larvae. We also created an open-source database containing sequencing information, survival curves, behavioral profiles and representative electrophysiology data. We offer all zebrafish lines as a resource to the neuroscience community and envision them as a starting point for further functional analysis and/or identification of new therapies.

Efficacy and tolerability of clobazam in adults with drug-refractory epilepsy

ObjectivesTo evaluate the effectiveness and tolerability of clobazam as an adjunctive treatment for adults with drug-resistant epilepsy.MethodsWe performed a single-center, retrospective chart review of patients ≥18 years of age with drug-resistant epilepsy who started clobazam between 2010 and 2018. Included patients had outpatient visits both before and ≥1 month after clobazam initiation. Epilepsy classification, seizure frequency before and after clobazam, duration of clobazam treatment, and adverse effects were analyzed.ResultsA total of 417 patients met inclusion criteria. Mean age was 37.5 years, and 54% of patients were female. Patients were on a mean of 2.4 antiepileptic drugs at time of initiation of clobazam. Epilepsy types were focal (56.8%), Lennox-Gastaut syndrome (LGS) (21.1%), generalized (15.1%), and unclassified (7.0%). At the first follow-up visit ≥1 month after clobazam initiation, 50.3% of patients had >50% reduction in seizure frequency, and 20.5% were seizure-free. Of the initial cohort, 17.1% were followed >1 year and were seizure-free at last follow-up. Response rates did not differ between different epilepsy classifications. Fifty-one percent of patients experienced ≥1 side effect, most commonly lethargy/fatigue (30.7%) or mood changes (10.8%). A total of 178 (42.6%) patients discontinued clobazam, most commonly due to adverse effects (55%).ConclusionsClobazam is effective and safe as a long-term adjunctive therapy for adults with drug-resistant epilepsy; efficacy in off-label use is similar to that in LGS.Classification of evidenceThis study provides Class IV evidence that clobazam is an effective treatment for adults with drug-resistant epilepsy, independent of epilepsy classification.