• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Annals of Clinical Neurophysiology
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• 제23권2호
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• pp.69-81
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• 2021

Cortico-cortical evoked potential (CCEP) mapping is a rapidly developing method for visualizing the brain network and estimating cortical excitability. The CCEP comprises the early N1 component the occurs at 10-30 ms poststimulation, indicating anatomic connectivity, and the late N2 component that appears at < 200 ms poststimulation, suggesting long-lasting effective connectivity. A later component at 200-1,000 ms poststimulation can also appear as a delayed response in some studied areas. Such delayed responses occur in areas with changed excitability, such as an epileptogenic zone. CCEP mapping has been used to examine the brain connections causally in functional systems such as the language, auditory, and visual systems as well as in anatomic regions including the frontoparietal neocortices and hippocampal limbic areas. Task-based CCEPs can be used to measure behavior. In addition to evaluations of the brain connectome, single-pulse electrical stimulation (SPES) can reflect cortical excitability, and so it could be used to predict a seizure onset zone. CCEP brain mapping and SPES investigations could be applied both extraoperatively and intraoperatively. These underused electrophysiologic tools in basic and clinical neuroscience might be powerful methods for providing insight into measures of brain connectivity and dynamics. Analyses of CCEPs might enable us to identify causal relationships between brain areas during cortical processing, and to develop a new paradigm of effective therapeutic neuromodulation in the future.

• 융합신호처리학회논문지
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• 제20권4호
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• pp.238-244
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• 2019

본 논문에서는 사이버 멀미 발생 예측에 활용 할 CNN 기반의 신경망의 성능 향상을 위하여, 다양한 형태의 신경망 구조에 대한 성능 분석을 수행한다. 특히, 대뇌 구조의 특성을 반영한 CNN을 차별적으로 구현하여 각 CNN(Convolution Neural Network)의 성능을 비교 분석하였으며, 이를 기반으로 사이버 멀미 발생 예측에 최적화된 신경망 구조의 설계와 관련한 기본적인 이론을 제시한다. 사이버 멀미 발생에는 많은 원인이 있지만 가장 중요한 원인은 뇌와 관련된 전정 기능의 장애에 기인한 것으로 판단된다. 뇌파는 뇌 활동 상태를 나타내는 지표 역할을 하며 외부 자극과 뇌 활동에 따라 차이를 나타낸다. 2019년에 출판된 Tony Ro의 Martijn E. Wokke 논문을 포함한 많은 연구와 실험에 의해 외부 자극과 뇌 활동으로 인한 뇌파의 변화가 입증되었으며, 본 논문에서는 이러한 상관관계를 바탕으로 사이버 멀미 유발 환경에서 수집 한 뇌파 데이터를 분석하고 뇌 구조의 특성을 반영하는 사이버 멀미 예측 인공 신경망의 구현 가능성을 제시하였다. 본 연구의 결과는 사이버 멀미 예측에 활용되는 CNN의 최적 성능 도출을 위하여, 고려하여야 할 신경망의 기본 구조 설계에 활용될 수 있으며, 다양한 가상현실(VR) 환경 등 대뇌 활동이 관여하는 분야에서 응용 될 신경망 구조 설계의 기초를 제공 할 것으로 기대된다.

• 대한의용생체공학회:의공학회지
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• 제28권1호
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• pp.153-161
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• 2007

Stroke is the second most significant disease leading to death in Korea. The conventional therapeutic approach is mainly based on physical training, however, it usually provides the limited degree of recovery of the normal brain function. The electric stimulation therapy is a novel and candidate approach with high potential for stroke recovery. The feasibility was validated by preliminary rat experiments in which the motor function was recovered up to 80% of the normal performance level. It is thought to improve the neural plasticity of the nerve tissues around the diseased area in the stroked brain. However, there are not so much research achievements in the electric stimulation for stroke recovery as for the Parkinson's disease or Epilepsy. This study aims at the developments of a wireless variable pulse generator using ZigBee communication for future implantation into human brain. ZigBee is widely used in wireless personal area network (WPAN) and home network applications due to its low power consumption and simplicity. The developed wireless pulse generator controlled by ZigBee can generate various electric stimulations without any distortion. The electric stimulation includes monophasic and biphasic pulse with the variation of shape parameters, which can affect the level of recovery. The developed system can be used for the telerehabilitation of stroke patient by remote control of brain stimulation via ZigBee and internet. Furthermore, the ZigBee connection used in this study provides the potential neural signal transmission method for the Brain-Machine Interface (BMI).

• Interdisciplinary Bio Central
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• 제1권1호
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• pp.4.1-4.7
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• 2009

Introduction: Histone modifications and DNA methylation are the major factors in epigenetic gene regulation. Especially, revealing how histone modifications are related to DNA methylation is one of the challenging problems in this field. In this paper, we address this issue and propose several plausible mechanisms for precise controlling of DNA methylation status at CpG islands. Materials and Methods: To establish the regulatory relationships, we used 38 histone modification types including H2A.Z and CTCF, and DNA methylation status at CpG islands across chromosome 6, 20, and 22 of human CD4+ T cell. We utilized Bayesian network to construct regulatory network. Results and Discussion: We found several meaningful relationships supported by previous studies. In addition, our results show that histone modifications can be clustered into several groups with different regulatory properties. Based on those findings we predicted the status of methylation level at CpG islands with high accuracy, and suggested core-regulatory network to control DNA methylation status.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.505-508
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• 1995

The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

• Journal of Communications and Networks
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• 제4권4호
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• pp.292-301
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• 2002

Wireless LAN technology is complementary to 3G systems and could be used to provide high bandwidth hotspot coverage, for example in railway stations and offices, in order to provide the high bandwidth video and broadband services such as those emerging on DSL fixed access. The IST Projects BRAIN and MIND have investigated a number of key technical enablers for such a system beyond 3G. These include scenarios and business models, design of an all-IP access network, consideration of ad hoc network extensions, enhancing Wireless LAN efficiency and compatibility with IP and, finally, terminal middleware and signalling for rapid adaptations to network QoS changes.

• Molecules and Cells
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• 제43권4호
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• pp.331-339
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• 2020

Genetic modifications in noncoding regulatory regions are likely critical to human evolution. Human-accelerated noncoding elements are highly conserved noncoding regions among vertebrates but have large differences across humans, which implies human-specific regulatory potential. In this study, we found that human-accelerated noncoding elements were frequently coupled with DNase I hypersensitive sites (DHSs), together with monomethylated and trimethylated histone H3 lysine 4, which are active regulatory markers. This coupling was particularly pronounced in fetal brains relative to adult brains, non-brain fetal tissues, and embryonic stem cells. However, fetal brain DHSs were also specifically enriched in deeply conserved sequences, implying coexistence of universal maintenance and human-specific fitness in human brain development. We assessed whether this coexisting pattern was a general one by quantitatively measuring evolutionary rates of DHSs. As a result, fetal brain DHSs showed a mixed but distinct signature of regional conservation and outlier point acceleration as compared to other DHSs. This finding suggests that brain developmental sequences are selectively constrained in general, whereas specific nucleotides are under positive selection or constraint relaxation simultaneously. Hence, we hypothesize that human- or primate-specific changes to universally conserved regulatory codes of brain development may drive the accelerated, and most likely adaptive, evolution of the regulatory network of the human brain.

• 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.

• 전자공학회지
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• 제42권2호
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• pp.40-44
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• 2015