• 대한예방한의학회지
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• 제14권2호
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• pp.135-148
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• 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.

• 한국정보전자통신기술학회논문지
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• 제12권5호
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• pp.521-528
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• 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
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• 제8권4호
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• pp.373-382
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• 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
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• 제9권3호
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• pp.1060-1071
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• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권12호
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• pp.587-594
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• 2021

뇌파(electroencephalogram, EEG)를 이용한 진단이 확대되면서 EEG 신호를 자동으로 분류하기 위한 다양한 연구가 활발히 이루어지고 있다. 본 논문은 일반인과 뇌전증 환자에게서 추출한 EEG 신호를 효과적으로 식별할 수 있는 CNN 모델을 제안한다. CNN의 학습에 필요한 데이터를 확장하기 위하여 EEG 신호를 낮은 차원의 신호로 분할하고, 이것을 다시 여러 개의 세그먼트로 중첩 분할하여 CNN 학습에 이용한다. 이와 더불어 CNN의 성능을 개선하기 위하여 CNN 앙상블 전략을 제안한다. 공개된 Bonn 데이터세트로 실험을 수행한 결과 뇌전증 발작을 99.0% 이상의 정확도로 검출하였고, 앙상블 방식에 의해 3-클래스와 5-클래스의 EEG 분류에서 정확도가 향상되었다.

• 동의생리병리학회지
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• 제21권1호
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• pp.328-332
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• 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
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• 제2권1호
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• pp.103-111
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• 1985

저자들은 1983년 5월부터 1985년 11월까지 만 30개월간 본원 소아과를 통해 진료받은 100명의 소아간질환아를 대상으로 관찰한 결과 다음과 같은 성적을 얻었다. 1. 남녀별 발생빈도는 1.2:1로 남아에서 약간 많았다. 2. 경련발생 연령은 6개월 미만이 13예(12.6%), 6개월~3세군이 34예(33.0%), 3~5세군이 16예(15.5%), 5~10세군이 24예(23.3%), 10~15세군이 16예 (15.5%)였다. 3. 간질경련 양상은 generalized tonic-clonic, tonic, clonic seizure가 49.5%, 간대성 근경련이 5.8%, 비전형 소발작이 5.8%, 이완성발작이 1%였고, simple P.S.가 7.8%, complex P.S.가 3.9%, simple P.S. $\overline{c}$ 2nd G.이 17.5%, complex P.S. $\overline{c}$ 2nd G.이 2.9%, 미분류가 5.8%였다. 4. 간질의 원인으로 추정이 가능했던 경우가 17예(16.5%)였는데 주산기 저산소증(4.9%), 뇌막염(3.9%), 미숙아분만(1.9%) 등이 많은 원인이었다. 5. 간질과 동반된 질환은 30예(29%)에서 보였는데 지능장애, 과다행동증, 운동발달지연, 뇌성마비 등이 많았다. 6. 42예에서 행한 뇌 전산화단층촬영에서 14예의 이상소견을 보였는데 뇌 위축이 6예, 뇌경색이 3예, 수두증 및 뇌부종소견이 각각 2 예씩 나타났다.

• Annals of Clinical Neurophysiology
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• 제9권2호
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• pp.59-62
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• 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
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• 제9권2호
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• pp.63-68
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• 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.

• 디지털융복합연구
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• 제14권4호
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• pp.277-283
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• 2016

본 논문에서는 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions; NEWFM) 기반의 웨이블릿 변환(wavelet transform)과 힐버트 변환(Hilbert transform)에 의해 추출한 첨점(peak)을 사용하여 뇌파(EEG)로부터 정상 파형과 간질 파형을 분류하는 새로운 방안을 제안하였다. NEWFM의 입력을 추출하는데 다음과 같은 3개의 단계가 수행되었다. 첫 번째 단계에서는 뇌파로부터 잡음을 제거하기 위해서 웨이블릿 변환을 사용하였다. 두 번째 단계에서는 웨이블릿 계수로부터 첨점(peak)을 추출하기 위해서 힐버트 변환을 사용하였다. 또한 크기가 큰 첨점을 추출하기 위해서 첨점의 평균값보다 큰 첨점만을 선택하였다. 세 번째 단계에서는 통계적 방법을 이용하여 첨점으로부터 NEWFM의 입력으로 사용할 16개의 특징을 추출하였다. NEWFM은 이들 16개의 특징을 입력으로 사용하여 99.25%, 99.4%, 99%의 정확도, 특이도, 민감도를 각각 구하였다. 향후 연구에서는 특징선택을 이용하여 16개의 특징으로부터 좋은 특징을 선택하여 정확도를 향상시킬 계획이다.