• 대한의용생체공학회:의공학회지
• /
• 제42권4호
• /
• pp.150-158
• /
• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• 환경생물
• /
• 제27권1호
• /
• pp.95-99
• /
• 2009

Transcriptomic changes in the brain of Limanda yokohamae were investigated to understand the environmental condition of Masan Bay, Korea. Differentially expressed genes (DEGs) in the brain of the flat fish from Masan Bay were identified by comparing those from the reference site Gangneung using annealing control primers-based polymerase chain reaction. The results demonstrated that two different kinds of the cytoplasmic ribosomal proteins, 40 s ribosomal protein S27a and ribosomal protein L6, were identified by the BLAST searching followed by sequence analysis. These findings suggest that environmental status of Masan Bay could hinder protein synthesis that is required for maintaining brain functions and thus cause the dysfunction of fish physiology.

• 만화애니메이션 연구
• /
• 통권38호
• /
• pp.71-91
• /
• 2015

본 연구는 애니메이션의 전문적인 특성을 고려한 라이프 드로잉 교수법을 연구하는 과정으로 두뇌의 창작 기제를 고려한 전략을 적용한 모형과 교수방법 설계를 목적으로 한다. 최근 들어 창의성을 기반으로 하는 각 전문분야의 교육방법에 대한 대안적인 논의로 뇌 기반 학습원리를 적용한 융합적 교수법에 대한 연구결과들이 발표되고 있다. 즉, 뇌의 창의기제를 기반으로 한 융합적 교육은 미술과 드로잉 교육뿐만 아니라, 예술전반에서 적용되고 있는 것이다. 라이프 드로잉은 인체에 대한 구조적 지식을 넘어서 인지적 감각, 창의성, 그리고 동작을 통한 대상과의 소통방식을 이해한 생동감 표현법 등을 숙련할 수 있는 종합적인 교수법을 요하는 분야이다. 이에 본 연구에서는 연구의 앞선 단계에서 분석된 창의, 학습기제와 내용요소를 바탕으로 하여 라이프 드로잉 숙련을 위한 전략과 방법 그것을 정리한 교육모형 구조도를 설계하여 본다. 그 결과 이전 연구의 결과물인 뇌의 창의, 학습 기제를 기반으로 한 라이프드로잉의 능력요소와 두뇌기반 촉진요소가 유기적으로 결합되기 위해서는 5단계 인지전략단계인 뇌 활성화 준비단계, 대뇌피질 기능 활성화, 고등사고촉진단계, 고등사고단계, 통합단계를 거쳤을 때 가능하다는 결론에 도달하였다. 또한 이를 실행하기위한 전략적 방법으로는 브레인짐(brain gym), 우뇌활성화드로잉, HSP(고차인지)트레이닝으로 설계되었다. 이를 토대로 하여 설계된 교수학습모형 구조도는 이후의 연구에서 해당 회기 동안의 교수학습지도안 설계로 이어진다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.46.3-46
• /
• 2001

The overall aim of this research is to develop a computer communication interface based on brain-wave bio potentials for physically disabled people. The work focuses on using EOG and EMG signals to input characters one by one using cursor movements on a GUI screen. The Cyberlink TM system is used to acquire brain waves in real time with electrodes. EMG and EOG signals are used to direct a cursor in order to select, or to click on a character on the screen. We present a novel method for automatic EOG pattern detection by using wavelet transforms with a neuro-fuzzy approach ...

• Progress in Superconductivity
• /
• 제13권3호
• /
• pp.139-145
• /
• 2012

Electric activity of cardiac muscles generates magnetic fields. Magnetocardiography (or MCG) technology, measuring these magnetic signals, can provide useful information for the diagnosis of heart diseases. It is already about 40 years ago that the first measurement of MCG signals was done by D. Cohen using SQUID (superconducting quantum interference device) sensor inside a magnetically shielded room. In the early period of MCG history, bulky point-contact RF-SQUID was used as the magnetic sensor. Thanks to the development of Nb-based Josephson junction technology in mid 1980s and new design of tightly-coupled DC-SQUID, low-noise SQUID sensors could be developed in late 1980s. In around 1990, several groups developed multi-channel MCG systems and started clinical study. However, it is quite recent years that the true usefulness of MCG was verified in clinical practice, for example, in the diagnosis of coronary artery disease. For the practical MCG system, technical elements of MCG system should be optimized in terms of performance, fabrication cost and operation cost. In this review, development history, technical issue, and future development direction of MCG technology are described.

• 한국콘텐츠학회논문지
• /
• 제3권3호
• /
• pp.85-97
• /
• 2003

MRI, CT, MRI, PET, SPECT, fMRI 등과 같은 단층의료영상은 병원에서 환자의 진단 및 치료 임상적 연구에서 폭넓게 사용되고 있다. 동일한 대상에 대하여 서로 다른 정보를 얻거나 비교를 하기 위하여 서로 다른 영상양식으로 촬영하거나 시간적 간격을 두고 단층영상을 획득하는 경우가 많다. 3차원 영상정합은 비교하고자 하는 두 영상을 하나의 3차원 좌표 공간으로 지도화하는 것이며, 크게 마커기반 정합과 특징기반 정합으로 분류된다. 뇌 영상의 3차원 정합은 뇌 수술부위 선정, 뇌 기능 연구, 뇌 지도화 연구 등에서 시각적 분석과 정량적 분석에서 중요한 위치를 차지한다. 본 논문에서는 뇌의 단층영상에 대하여 흔히 사용되고 있는 3차원 정합인 마커기반 정합법과 특징기반 정합법에 대하여 소개하고 이에 대한 비교 고찰을 행하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권4호
• /
• pp.1543-1561
• /
• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• 한국정보디스플레이학회:학술대회논문집
• /
• 한국정보디스플레이학회 2005년도 International Meeting on Information Displayvol.II
• /
• pp.1056-1058
• /
• 2005

Electrical and optical characteristics of MEH-PPV-based PLEDs with the LiF anode interfacial layer were investigated. The maximum luminance efficiency of the device with a LiF anode interfacial layer of 1-nm-thick was 3.0 lm/W, which is higher than 1.97 lm/W of the device without a LiF layer. By inserting LiF, excess injected holes from ITO anode can be blocked and hence the recombination ratio of electrons and holes can be increased in the emitting layer to improve device efficiency.

• 한국콘텐츠학회논문지
• /
• 제16권4호
• /
• pp.36-44
• /
• 2016

우리나라의 한정된 예산과 연구자원을 활용하여 뇌과학 분야 연구성과를 극대화하기 위해서는 뇌연구 분야 중 강점을 가질 수 있는 융합연구 분야를 선정하여 우선적으로 육성할 필요가 있다. 본 연구에서는 선도가능성, 혁신성, 기대성과 등을 바탕으로 중점 연구분야의 우선순위를 결정함으로써 뇌과학 정책 결정에 가이드라인을 제공하고자 한다. 이를 위해 우선 뇌과학 분야 국내 리더급 연구자들에게 뇌과학 중점 연구분야 후보로 5개 영역, 즉 신경교세포, 뇌정밀의학, 신경후성유전학, 뇌신경모사컴퓨터, 뇌파이용 대상 구동기술을 추천받았다. 그 다음 AHP를 통해 중점후보 분야와 평가항목 간의 상대적 가중치를 결정하고 그 우선순위를 결정하였다. 평가 항목 중 선도가능성 등의 세부항목이 포함된 유망도가 중점분야 선정에 있어 가장 중요한 평가 요소인 것으로 나타났으며, 이러한 평가 요소로 비교 분석해본 결과 중점 연구분야 후보 중에 신경교세포의 가중치가 가장 높은 것으로 나타나 관련 정책 추진시 가장 우선적으로 고려해야 할 것으로 판단된다.

• 대한의용생체공학회:의공학회지
• /
• 제45권1호
• /
• pp.26-36
• /
• 2024

Functional brain imaging techniques have been used to diagnose psychiatric disorders such as dementia, depression, and autism. Recently, these techniques have also been actively used to study hearing loss. The present study reviewed the application of the functional brain imaging techniques in auditory research, especially those focusing on hearing loss, over the past decade. EEG, fMRI, fNIRS, MEG, and PET have been utilized in auditory research, and the number of research studies using these techniques has been increasing. In particular, fMRI and EEG were the most frequently used technique in auditory research. EEG studies mostly used event-related designs to analyze the direct relationship between stimulus and the related response, and in fMRI studies, resting-state functional connectivity and block designs were utilized to analyze alterations in brain functionality in hearing-related areas. In terms of age, while studies involving children mainly focused on congenital and pre- and post-lingual hearing loss to analyze developmental characteristics with and without hearing loss, those involving adults focused on age-related hearing loss to investigate changes in the characteristics of the brain based on the presence of hearing loss and the use of a hearing device. Overall, ranging from EEG to PET, various functional brain imaging techniques have been used in auditory research, but it is difficult to perform a comprehensive analysis due to the lack of consistency in experimental designs, analysis methods, and participant characteristics. Thus, it is necessary to develop standardized research protocols to obtain high-quality clinical and research evidence.