• Journal of Society of Preventive Korean Medicine
• /
• v.14 no.2
• /
• pp.135-148
• /
• 2010

The major purpose of this study is to evaluate the classification of epileptic seizure types and epilepsy described in the literatures of both Western and East Asian medicine, especially based on the two criteria- a theoretical and a practical aspect of the classification systems. Currently, the 1981 classification of epileptic seizure types, and the 1989 classification of epilepsy syndromes and epilepsies which were proposed and approved by the International League Against Epilepsy(ILAE) have been generally accepted worldwide, although a variety of modifications have been consistently suggested. A large proportion of epilepsy cases cannot be easily classified as either 'focal' or 'generalized' or as either 'symptomatic' or 'idiopathic', so they fail to be precisely fallen into any of the ILAE categories. Terms and concepts used in the East Asian medicine are also inadequate to identify epileptic seizure types and epilepsy syndromes as discrete diagnostic entities because of ambiguities in definition and use. Therefore, this article suggests an alternative approach not only more helpful in understanding mechanism of epilepsy but also more easily applicable and effective in clinical value.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.5
• /
• pp.521-528
• /
• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• Biomedical Engineering Letters
• /
• v.8 no.4
• /
• pp.373-382
• /
• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.3
• /
• pp.1060-1071
• /
• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.12
• /
• pp.587-594
• /
• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.21 no.1
• /
• pp.328-332
• /
• 2007

Epilepsy is any of various neurological disorders characterized by sudden, recurring attacks of motor, sensory, or psychic malfunction with or without loss of consciousness or convulsive seizures. It could be divided into subcategories due to the international classification of epileptic seizure and the complex partial seizure, that is one of epileptic seizure subcategories, is characterized by elaborate and multiple sensory, motor, and/or psychic components accompanying the clouding of consciousness, prodrome, automatism, postictal confusion. This study reports a patient who was presumptive diagnosed as complex partial seizure by having the clouding of consciousness, prodrome, postictal confusion. We also diagnosed him as a ‘dianxian’ patient induced by sum deficiency of liver & kidney. This patient was treated by acupuncture to tonifying eum of liver & kidney and it achieved markedly improved symptoms.

• Journal of Yeungnam Medical Science
• /
• v.2 no.1
• /
• pp.103-111
• /
• 1985

Childhood epilepsy which has high prevalence rate and inception rate is one of the commonest problem encountered in pediatrician. In contrast with epilepsy of adult, in childhood epilepsy, more variable and varying manifestations are found because the factors of age, growth and development exert their influences in the manifestations and the courses of childhood epilepsy. Moreover epileptic children have associated problems such as physical and mental handicaps, psychologicaldisorders and learning disability. For these reasons pediatrician who deals with epileptic children experiences difficulties in making diagnosis and managing them. In order to improve understanding and management of childhood epilepsy, authors reviewed 103 cases of epileptic patients seen at pediatric department of Yeungnam University Hospital retrospectively. The patients were classified according to the type of epileptic seizure. Suspected causes of epilepsy, associated conditions of epileptic patients, age incidence and the findings of brain CT were reviewed. Large numbers of epileptic patients (61.2%) developed their first seizures under the age of 5. The most frequent type of epileptic seizure was generalized ionic-clonic, tonic, clonic seizure (49.5%), followed by simple partial seizure with secondary generalization (17.5%), simple partial seizure (7.8%), a typical absence (5.8%) and unclassified seizure (5.8%). In 83.5% of patients, we could not find specific cause of it, but in 16.5% of cases, history of neonatal hypoxia (4.9%), meningitis (3.9%), prematurity (1.9%), small for gestational age (1.0%), CO poisoning (1.0%), encephalopathy (1.0%), DPT vaccination (1.0%), cerebrovascular accident (1.0%) and neonatal jaundice (1.0%) were found, 30 cases of patients had associated diseases such as mental retardation, hyperactivity, delayed motor milestones or their combinations. The major abnormal findings of brain CT performed in 42 cases were cortical atrophy, cerebral infarction, hydrocephalus and brain swelling. This review stressed better designed classification of epilepsy is needed and with promotion of medical care, prevention of epilepsy is possible in some cases. Also it is stressed that childhood epilepsy requires multidisplinary therapy and brain CT is helpful in the evaluation of epilepsy with limitation in therapeutic aspects.

• Annals of Clinical Neurophysiology
• /
• v.9 no.2
• /
• pp.59-62
• /
• 2007

Electroencephalogram (EEG) is an indispensable tool for diagnosis of epilepsy and is the only assisting barometer of complete remission of epilepsy, which means prolonged, persistent suppression of cortical excitement in epileptic focus in addition to the clinical control of epileptic seizure. The specific morphologies or distribution of epileptic form discharges give us good information for the classification of seizure or epilepsy and epileptic syndromes, which consists of "Pros." in terms of diagnostic approach. In contrast, the EEG as a tool for long-term follow up might be limited due to the various clinical situation of each patient, which consists of "Cons." in terms of the usefulness of EEG for clinical decision. "Cons." aspect of EEG, which clinicians are more frequently coped with than those of "Pros", is an obstacle of utilization of follow up EEG in clinical practice. This is an overview about controversies in usefulness of EEG and the detailed aspects of "Pros." and "Cons." of EEG for clinical decision will be discussed following two articles. We tried to make consensus for the usefulness of EEG especially in the situation of "Cons." with plausible guideline.

• Annals of Clinical Neurophysiology
• /
• v.9 no.2
• /
• pp.63-68
• /
• 2007

The EEG plays an important diagnostic role in epilepsy and provides supporting evidence of a seizure disorder as well as assisting with classification of seizures and epilepsy syndromes. There are a variety of electroclinical syndromes that are really defined by the EEG such as Lennox-Gastaut syndrome, benign rolandic epilepsy, childhood absence epilepsy, juvenile myoclonic epilepsy and also for localization purposes, it is vitally important especially for temporal lobe epilepsy. The sensitivity of first routine EEG in diagnosis of epilepsy has been known about 20-50%, but this proportion rises to 80-90% if sleep EEG and repetitive recording should be added. Convincing evidences suggest that the EEG may also provide useful prognostic information regarding seizure recurrence after a single unprovoked attack and following antiepileptic drug (AED) withdrawal. Moreover, patterns in the EEG make it possible to disclose an ictal feature of nonconvulsive status epilepticus, separate epileptic from other non-epileptic episodes and clarify the clues predictive of the cause of the encephalopathy (i.e., triphasic waves in metabolic encephalopathy). Therefore, regardless of its low sensitivity and other pitfalls, EEG should be considered not only in the situation of new onset episode such as a newly developed, unprovoked seizure or a condition manifesting decreased mentality from obscure origin, but also as a barometer of the long-term outcome following AED withdrawal.

• Journal of Digital Convergence
• /
• v.14 no.4
• /
• pp.277-283
• /
• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.