• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.301-303
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• 1996

A platform is developed for fast and effective functional mapping of human brain, which can allow semi-automatically the whole processes of an image segmentation, a fusion of MR and PET images, and 3-D rendering of volumetric data, including DICOM-based image transfers from PACS archiver within a short period of time.

• Psychiatry investigation
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• v.15 no.11
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• pp.1071-1078
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• 2018

Objective We developed easily accessible imagery-based treatment program for patients with post-traumatic stress disorder (PTSD) related to workplace accidents and investigated the effects of the program on various PTSD related symptoms. Methods The program was based on an online platform and consisted of eight 15-min sessions that included script-guided imagery and supportive music. Thirty-five patients with workplace-related PTSD participated in this program 4 days per week for 4 weeks. Its effects were examined using self-report questionnaires before and after the take-home online treatment sessions. Results After completing the 4-week treatment program, patients showed significant improvements in depressed mood (t=3.642, p=0.001) based on the Patient Health Questionnaire-9 (PHQ-9), anxiety (t=3.198, p=0.003) based on the Generalized Anxiety Disorder seven-item (GAD-7) scale, and PTSD symptoms (t=5.363, p<0.001) based on the Posttraumatic Stress Disorder Check List (PCL). In particular, patients with adverse childhood experiences exhibited a greater degree of relief related to anxiety and PTSD symptoms than those without adverse childhood experiences. Conclusion The present results demonstrated that the relatively short online imagery-based treatment program developed for this study had beneficial effects for patients with workplace-related PTSD.

• Journal of Biomedical Engineering Research
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• v.29 no.5
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• pp.355-363
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• 2008

In order to extract only the alpha activity related signals from EEG recordings, we have applied Constrained Independent Component Analysis (cICA), a new extension of ICA in which some a priori knowledge of the alpha activity is utilized to extract only desired components. Its extraction (or filtering) performance has been compared to that of the conventional band-pass filtering via the scalp alpha power maps and cortical source maps of the alpha activity. Our results demonstrate that the alpha power maps and cortical source maps from the cICA-extracted alpha signals reveal more focalized alpha generating regions of the brain than those from the band-pass filtered alpha EEG signals. Furthermore they match more closely the activated regions of the brain mapped using fMRI, validating our results. We believe that the cICA-based filtering approach of EEG signals is a more effective means of extracting a specific brain activity reflected in EEG signals that will result in more accurate source localization or imaging maps.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.1
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• pp.59-72
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• 2010

Purpose: This study is a descriptive research to analyze prognostic factors of barbiturate coma therapy (BCT) for severe brain damage patients, to develop a critical pathway (CP) based on the results of analysis and to examine the effect of its clinical application. Method: We analyzed medical records of 76 patients who received BCT for more than three days between January 1999 to July 2005. Based on the results of the analysis, we developed a CP and applied it to 12 people during August-December of 2005. Result: By application of BCT CP, the mortality rate decreased from 31.6% to 16.7%. It was found that the period of staying at ICU and total period of hospitalization were shortened by 2.78 (13.9%) days and 16.43 (29.4%) days, respectively. The Glasgow coma scale of the recovery group by CP application was 9.03 (4.64) at 72 hours post of BCT and 14.28 (1.82) at discharge from hospital, and DRS was 6.62 (6.38) points. Conclusion: By verifying clinical validity of the suggested CP, we believe that we have obtained visible effects standardizing the treatment pathway of BCT for brain damage patients.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.235-246
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• 2023

Objective: It is difficult to predict the treatment response of tissue after stereotactic radiosurgery (SRS) because radiation necrosis (RN) and tumor recurrence can coexist. Our study aimed to predict tumor recurrence, including the recurrence site, after SRS of brain metastasis by performing a longitudinal tumor habitat analysis. Materials and Methods: Two consecutive multiparametric MRI examinations were performed for 83 adults (mean age, 59.0 years; range, 27-82 years; 44 male and 39 female) with 103 SRS-treated brain metastases. Tumor habitats based on contrast-enhanced T1- and T2-weighted images (structural habitats) and those based on the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) images (physiological habitats) were defined using k-means voxel-wise clustering. The reference standard was based on the pathology or Response Assessment in Neuro-Oncologycriteria for brain metastases (RANO-BM). The association between parameters of single-time or longitudinal tumor habitat and the time to recurrence and the site of recurrence were evaluated using the Cox proportional hazards regression analysis and Dice similarity coefficient, respectively. Results: The mean interval between the two MRI examinations was 99 days. The longitudinal analysis showed that an increase in the hypovascular cellular habitat (low ADC and low CBV) was associated with the risk of recurrence (hazard ratio [HR], 2.68; 95% confidence interval [CI], 1.46-4.91; P = 0.001). During the single-time analysis, a solid low-enhancing habitat (low T2 and low contrast-enhanced T1 signal) was associated with the risk of recurrence (HR, 1.54; 95% CI, 1.01-2.35; P = 0.045). A hypovascular cellular habitat was indicative of the future recurrence site (Dice similarity coefficient = 0.423). Conclusion: After SRS of brain metastases, an increased hypovascular cellular habitat observed using a longitudinal MRI analysis was associated with the risk of recurrence (i.e., treatment resistance) and was indicative of recurrence site. A tumor habitat analysis may help guide future treatments for patients with brain metastases.

• The Journal of Internal Korean Medicine
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• v.30 no.3
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• pp.550-557
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• 2009

Objectives : The purpose of this study was to investigate the possibility of Bi-Su as a pattern identification (PI) index in stroke patients. Methods : The subjects were 424 hospitalized stroke patients within 1 month from onset and diagnosed with the same PI subtypes (dampness & phlegm, qi deficiency, fire & heat, eum deficiency, and blood stasis) by agreement of two clinical experts. Bi-Su type is a kind of body shape (Bi : fat, Su : lean). Bi-Su type and degree (Bi-Su score) were decided by clinical expert. Body mass index (BMI) and waist-hip ratio (WHR) were used as an obesity index. Correlation analysis between Bi-Su score and obesity index (Spearman) and variance analysis for Bi-Su score, BMI, and WHR among PI subtypes (ANOVA) and sex were carried out. Results : While there was partial correlation between Bi-Su type and BMI($r^2$=0.634, p<0.001), the distribution of the BMI group based on the Bi-Su group showed the broadest range. The Bi-Su score in the dampness & phlegm group was higher than in the other groups (p<0.001). BMI in the dampness & phlegm groups was also higher but the BMI differences among PI subtypes was low (p=0.002). The Bi-Su score in the dampness & phlegm group was similar in both sexes, although the hand score in the eum deficiency group was the lowest, especially in males. Conclusions : Although BMI is not an objective enough tool for evaluating Bi-Su type, Bi-Su type is more appropriate than BMI as PI index. Therefore Bi-Su type could be used as one of the PI indices for dampness & phlegm or eum deficiency group in stroke patients.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.120-127
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• 2015

Mild Cognitive Impairment(MCI) is a prior step to Alzheimer's Disease(AD). It is different from AD which is seriously affecting daily life. Particularly, the hippocampus could be charged a crucial function for forming memory. MCI has a high risk about progress to AD. Our investigated research for a relationship between hippocampus and AD has been studied. The measurement of hippocampus volumetric is one of the most commonly used method. The three dimensional reconstructed medical images could be passible to interpret and its examination in various aspects but the cost of brain research with the medical equipment is very high. In this study, 3D visualization was performed from a series of brain Magnetic Resonance Images(MRI) and we have designed and implemented a competitive software tool based on the open libraries of Visualization ToolKit(VTK). Consequently, our visualization software tool could be useful to various medical fields and specially prognosis and diagnosis for MCI patients.

• Journal of Trauma and Injury
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• v.33 no.4
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• pp.236-241
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• 2020

The goal of this study was to evaluate the hypothesis that not every patient with hydrocephalus after decompressive craniectomy needs cerebrospinal fluid diversion, and that cranioplasty should be performed before considering cerebrospinal fluid diversion. Methods: Data were collected from 67 individual traumatic brain injury patients who underwent cranioplasty between January 1, 2019 and December 31, 2019. Patients' clinical and radiographic progression was reviewed retrospectively based on their medical records. Results: Twenty-two of the 67 patients (32.8%) had ventriculomegaly on computed tomography scans before cranioplasty. Furthermore, 38 patients showed progressive ventriculomegaly after cranioplasty. Of these 38 patients, only six (15.7%) showed worsening neurologic symptoms, which were improved by the tap test; these patients eventually underwent ventriculoperitoneal shunt placement. Conclusions: Cerebrospinal fluid diversion is not always required for radiologically diagnosed ventriculomegaly in traumatic brain injury patients after decompressive craniectomy. A careful clinical and neurologic evaluation should be conducted before placing a shunt.