23 research outputs found
Characteristics and Risk Factors of Aspiration in Lateral Medullary Infarction
Objective: To evaluate the characteristics of dysphagia and identify the risk factors of bolus aspiration in patientspresenting with pure lateral medullary infarction (LMI). Methods: Between January 2014 and January 2019, 51 post-stroke patients with LMI who underwent a videofluoroscopicswallowing study (VFSS) were enrolled retrospectively, and their medical records and brain magnetic resonanceimaging results were reviewed. The VFSS results were evaluated to analyze the swallowing function using thepenetration-aspiration scale, functional dysphagia scale, and imaging analysis software. Results: Bolus aspiration was detected in 21 patients (41.2%). The common abnormal VFSS findings were residue invalleculae (74.5%), delayed triggering of pharyngeal swallow (72.5%), residue in pyriform sinuses (62.7%), delayed pharyngealtransit time (56.9%), reduced laryngeal elevation (51.0%), and coating of the pharyngeal wall (49.0%). Theincidence of aspiration was significantly higher in the typical lesions (including the diagonal band-shaped lesions)and the large type lesions extending ventrally or dorsally, as compared to other lesion types (P๏ผ0.05). Logistic regressionanalyses revealed that the residue in pyriform sinuses is a significant independent risk factor of aspirationin the puree trial, and prolonged pharyngeal delay time (PDT) and residue in valleculae are significant risk factorsin the thin liquid trial (P๏ผ0.05). Conclusion: Considering all clinical factors, lesion locations, and swallowing processes, results of the current studyindicate that residue in pyriform sinuses is an independent risk factor of aspiration in the swallowing puree technique,whereas prolonged PDT and residue in valleculae are independent risk factors of aspiration in the swallowingliquid technique.ope
Trial of Metoclopramide on Oro-facial Dyskinesia Following Traumatic Brain Injury: A Case Report
Oro-facial dyskinesia is characterized by involuntary repetitive movements of the tongue, lip, or jaw, which is known to be derived by variable causes. Pre- and post-synaptic dopamine receptor abnormalities by degenerative changes in the brain seem to be the key pathophysiology, but the exact mechanism still remained to be unknown. Metoclopramide can pass the blood-brain barrier, which is known for a selective presynaptic autoregulating dopamine D2 receptor antagonist in the brain, and is usually prescribed for dyspepsia, nausea and vomiting. In particular, it was also reported to improve the symptoms of diurnal bruxism after brain injury. With reviewing some of literatures, we present a case of 27 year old man with traumatic brain injury who showed improvement of oro-facial dyskinesia after taking oral metoclopramide.ope
Change of Credit Supply Channel During the 2008 Global Financial Crisis
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ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ฒฝ์ ํ๋ถ, 2013. 2. ๊น์์.This paper elucidates that there exists (weak) substitution relationship between a bank loan (BL) channel and a corporate bond (CB) channel confirmed by analyses of multi-national data. The degree of substitution seems to be higher after the financial crisis. In particular, this counter-cyclicality between the BL and CB appears more clearly under the GDP shock. Two of key results, i) higher level of substitution between BL and CB channel after a macroeconomic impact ii) theconsistent counter-cyclicality between CB and GDP growth rates, provide a policy implication. Thus, if CB markets are developed and controlled efficiently with minimization of detrimental effects caused by default risks, a CB channel would play an important role in funding credits for firms when there is illiquidity in a bank financing channel.1. Introduction
2. Data Analysis
2.1. Data Description
2.2. Aggregate Level Analysis
2.2.1. North and South America Group
2.2.2. European Group: Core, PIIGS, and Others
2.2.3. Asia โ Pacific Group
2.3. Closer Look through Cross-Correlation Estimation, Multivariate Regression, and Granger Causality Test
2.3.1. Cross - Correlation Estimation
2.3.2. Regression Analysis
2.3.3. Granger Causality Test
2.4. Results and Discussion
3. Vector Autoregression (VAR) Model
3.1. VAR Model
3.2. Impulse - Response Analysis
4. Conclusion
References
APPENDIX I
APPENDIX II
APPENDIX IIIMaste
๋ฐฑ์ ๊ตญ์๋ํํ ๋ชจ๋ธ์์ ์ ์ฒด์จ์๋ฒ์ด NLRP3 ์ธํ๋ผ๋ง์ข ๋ฐํ์ ๋ฏธ์น๋ ์ํฅ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ํ๊ณผ, 2015. 2. ์ค๋ณ์ฐ.Background: Hypothermia is generally known to reduce brain injury following cerebral ischemia by various mechanisms including anti-inflammatory effects. However, the exact anti-inflammatory mechanism of hypothermia is not well known. Recently, several evidence have suggested that NLRP3 inflammasome is involved in the pathogenesis of sterile inflammatory response by processing caspase-1 and Interleukin (IL)-1ฮฒ to an active stage following cerebral ischemia. The association between hypothermia and inflammasome in the cerebral ischemia animal model is not reported. So we hypothesized that hypothermia can attenuate the expression of NLRP3 inflammasome in a rat model of focal cerebral ischemia.
Methods and Results: For in vitro study, BV-2 cells (immortalized mouse microglial cell line) were divided by two groups, control and oxygen-glucose deprivation groups. Each groups were re-divided by different temperatures, normothermia (37 ยฐC) and hypothermia (33 ยฐC). Hypothermia was maintained for 3 h in the beginning of study. Western blotting with antibodies to IL-1ฮฒ, IL-18 and NLRP3 inflammasome complexes including NLRP3 protein, ASC, and caspase-1 and immunohistochemical staining with caspase-1 antibody were done. Western blotting showed increased expression levels of IL-1ฮฒ, IL-18, NLRP3, ASC, and caspase-1 after OGD group and reduced significantly by hypothermia. Caspase-1 expressed in the cytosol of BV-2 cell in all groups and showed relatively decreased expression in OGD with hypothermia group compared with OGD with normothermia group.
For in vivo study, transient middle cerebral artery occlusion (MCAO) model was induced in male SpragueโDawley (SD) rats using the suture occlusion technique. Sixteen SD rats underwent left MCA occlusion for 2 h followed by reperfusion for 22 h. For the hypothermia, rats were cooled by alcohol spraying and evaporating methods within 15 min at the occlusion of MCA. During 2 h with occlusion period, rectal temperature was maintained with 33 ยฐC (hypothermia group) or 37 ยฐC (normothermia group). Rats were sacrificed at 24 h after ischemia. Nissl staining to evaluate the infarct volume showed reduced infarct volume in hypothermia group compared with the normothermia group (normothermia, 248 ยฑ 52 mm3hypothermia, 128 ยฑ 27 mm3p<0.001n=8 per group). To analyze the effects of hypothermia on the expression of IL-1ฮฒ, and NLRP3 proteins in ischemic brain tissue, we measured IL-1ฮฒ, and NLRP3 levels in ischemic or contralateral non-ischemic brain tissues. Compared with contralateral non-ischemic hemisphere, Western blot with IL-1ฮฒ and NLRP3 antibodies showed significantly increased expression levels in ischemic hemisphere (p<0.001 in normothermia groupsp<0.05 in hypothermia groups). IL-1ฮฒ and NLRP3 proteins were significantly decreased in ischemic brain tissue with hypothermia than ischemic brain tissue with normothermia (p<0.05 by Western blot with IL-1ฮฒ antibodyp<0.001 with NLRP3 antibody).
Conclusion: Our study revealed that NLRP3 inflammasome is overexpressed in the BV-2 microglial cells with OGD models, and hypothermia attenuates the NLRP3 inflammasome expression and infarct volume in a rat model of focal cerebral ischemia.Abstract in English
List of Figures
Contents
Introduction
Methods
Results
Discussion
References
Abstract in KoreanMaste
Effects of Cerebrolysinยฎ in Patients With Minimally Conscious State After Stroke: An Observational Retrospective Clinical Study
Introduction: The neurotrophic drug Cerebrolysin is composed of low-molecular-weight peptides and amino acids and has been shown to have neuroprotective and neuroplastic properties. Cerebrolysin has been reported to promote the recovery of motor functions in central nervous system disorders; however, the effects on the consciousness improvements in post-stroke patients have not yet been studied extensively. Therefore, we aimed to examine the effectiveness of Cerebrolysin on improving the consciousness level of stroke patients with minimally conscious state (MCS). Materials and Methods: In this retrospective study we included ischemic and/or hemorrhagic stroke patients with MCS according to the Coma Recovery Scale-Revised (CRS-R), who were admitted to our hospital between 2014 and 2017. All patients received comprehensive rehabilitation therapy including physical and occupational therapy. We compared patients treated with Cerebrolysin against patients who did not receive Cerebrolysin. Patients were included in the verum group if they received 10 mL of Cerebrolysin IV for at least 20 days. CRS-R scores were assessed at admission and discharge. Results: Of 1,531 patients screened, 75 were included in the study (Cerebrolysin, n = 43; control, n = 32). Baseline characteristics were similar between groups. At discharge, ~2 months after onset of stroke, Cerebrolysin-treated patients improved significantly in the CRS-R (p = 0.010) after adjustment for confounders using linear mixed model (LMM), especially in the Oromotor (p = 0.003) and Arousal subscales (p = 0.038). No safety issues were observed. Conclusion: This retrospective study suggests that Cerebrolysin may improve the level of consciousness in stroke patients with MCS, which should be further investigated in a well-designed, double-blind, placebo-controlled, randomized trial.ope
์ํ๋ํธ ์์ ํ ๋ฐ์ํ ํ์น์กฐ์ ๊ฒฝ ์์์ ํ๋ณต์์์ ๋ํ ํํฅ์ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ์น์ํ๋ํ์ : ์น์ํ๊ณผ, 2014. 2. ์ด์ข
ํธ.๋ณธ ์ฐ๊ตฌ๋ ์น๊ณผ ์ํ๋ํธ ์์ ๊ณผ์ ์์ ๋ฐ์ํ ํ์น์กฐ์ ๊ฒฝ์์ ํ์๋ค์ ๋ํ ํํฅ์ ์ฐ๊ตฌ๋ฅผ ํตํด ํ์๋ค์ ๋ํ ์ผ๋ฐ์ ์ธ ์์์ ์ธ ๊ฒฝํฅ์ ํ์
ํ๊ณ ํ์น์กฐ์ ๊ฒฝ์์ ์ ์ด๋ป๊ฒ ๋์ฒํ๋ ๊ฒ์ด ์ค์ ๋ก ํจ๊ณผ๊ฐ ์๋์ง ์์๋ณด๋ ๊ฒ์ ๊ทธ ๋ชฉ์ ์ผ๋ก ํ์๋ค.
2008๋
1์๋ถํฐ 2011๋
1์๊น์ง ์ํ๋ํธ ์์ ํ ํ์น์กฐ์ ๊ฒฝ์์์ ์ฃผ์๋ก ์์ธ๋ํ๊ต์น๊ณผ๋ณ์์ ๋ด์ํ 113๋ช
์ ํ์๋ค์ ๋์์ผ๋ก ํํฅ์ ์ผ๋ก ์ฐ๊ตฌํ์๋ค. 113๋ช
์ ๋ํ ํ์๋ค์ ์ฐจํธ์ ๋ฐฉ์ฌ์ ์์ ๋ถ์์ ํตํ์ฌ ์ฑ๋ณ, ๋์ด, ์ ๊ฒฝ์์์ ์ผ์ผํจ ์ํ๋ํธ์ ์๋ฆฝ๋ถ์, ์ํ๋ํธ์ ํ ์ฒ์น(์ํ๋ํธ ์ ๊ฑฐ, ์งง๊ฒ ํ๊ธฐ, ์ํ ์ ์ง), ๋ฐฉ์ฌ์ ์์์์ ์ํ๋ํธ์ ํ์น์กฐ์ ๊ฒฝ ๊ฐ์ ๊ฑฐ๋ฆฌ, ๋ด์ ์ ์ฒ์น, ์ด๊ธฐ์์ ํ ์์ธ๋ํ๊ต์น๊ณผ๋ณ์์ ๋ด์ํ๊ธฐ ๊น์ง ๊ฑธ๋ฆฐ ์๊ฐ์ ๋ํ ์ ๋ณด๋ฅผ ๋ชจ์ ์ด๋ฌํ ์์๋ค์ด ํ์์ ํ๋ณต๊ณผ ์ด๋ป๊ฒ ์ฐ๊ด์ฑ์ด ์๋์ง ์์๋ณด์๋ค. ํ์์ ํ๋ณต์ ๋๋ ๊ฐ๊ฐ ํ์๊ฐ ์ฃผ๊ด์ ์ผ๋ก ํธ์ํ ๋ด์ฉ๊ณผ ์์์ ์ผ๋ก ์ค์ํ ๊ฐ๊ด์ ์ธ ํ๊ฐ์ ๊ทผ๊ฑฐํ์ฌ ์กฐ์ฌํ์๋ค.
๊ทธ ๊ฒฐ๊ณผ ์ํ๋ํธ ์์ ์ค ํ์ง์ด์ ๊ฒฝ ์์์ด ๋ฐ์ํ๋ ๋น์จ์ ๋จ์๋ณด๋ค ์ฌ์๊ฐ 2๋ฐฐ ์ ๋๋ก ๋์์ผ๋ฉฐ ์ฐ๋ น๋ณ๋ก๋ 50๋์ 60๋๊ฐ ๋๋ถ๋ถ์ ์ฐจ์งํ์๋ค. ๋ํ ํ์
์ 2๋๊ตฌ์น ๋ถ์์ ์ํ๋ํธ๋ฅผ ์๋ฆฝํ๋ ๊ฒฝ์ฐ์ ํ์น์กฐ์ ๊ฒฝ์์์ด ๊ฐ์ฅ ํธ๋ฐํ์๋ค. ์์๋ ํ์น์กฐ์ ๊ฒฝ์ ํ๋ณต์ ์ํ๋ํธ๋ฅผ ์ ๊ฑฐํ๊ฑฐ๋ ์งง๊ฒ ํ๋ ์ฒ๋ฆฌ์ ํ์น์กฐ์ ๊ฒฝ์์์ด ๋ฐ์ํ ์ดํ ์์ธ๋ํ๊ต์น๊ณผ๋ณ์์ผ๋ก ๋ด์ํ๋๋ฐ ๊น์ง ๊ฑธ๋ฆฌ๋ ๊ธฐ๊ฐ์ ์ ์๋ฏธํ ์ฐ๊ด์ฑ์ ๋ํ๋ด์๋ค.I. ์๋ก 6
II. ํ์ ๋ฐ ๋ฐฉ๋ฒ 8
1. ํ์ 8
2. ํต๊ณ 9
III. ๊ฒฐ๊ณผ 10
IV. ๊ณ ์ฐฐ 13
V. ๊ฒฐ๋ก 15
์ฐธ๊ณ ๋ฌธํ 16Maste
Analysis on the Luminance Degradation of Organic Light-Emitting Diodes Doped with Fluorescent Dyes
ํ์๋
ผ๋ฌธ(์์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐ. ์ปดํจํฐ๊ณตํ๋ถ,2010.2.Maste
๋์กธ์ค ํ ๊ธฐ๋ฅ์ ์์กด์ฑ ๋ฐ ์ฌ๋ง๋ฅ : ์ฐ๋ น๋ณ ์ฅ๊ธฐ ์ํ
Introduction: The prognosis after stroke according to onset age have been dealt in many studies. However, previous studies had limitations in that data was gathered with a relatively short total follow-up period, fewer participants, and fewer assessment iterations. The purpose of this study was to define the specific ages of stroke-onset, which determine the prognosis after stroke, and to elucidate the relationship between the age at stroke onset and long-term poststroke functional dependence and risk of death using large-scale, multi-center longitudinal cohort data. Methods: Of the total 10,636 patients registered in the Korean Stroke Cohort for Function and Rehabilitation from August 2012 to May 2015, patients who completed follow-ups for four-year follow-up after stroke and met inclusion criteria were enrolled. To classify all patients into one to three groups with similar prognostic trajectories of Functional Independence Measure (FIM) and modified Rankin Scale (mRS) scores, and mortality risk, models adjusted for covariates were constructed with all possible combinations. Of these, the models with the best goodness-of-fit were selected for each dependent variable. Then, to investigate the time points with the most abrupt change in the effect of age groups on the FIM, mRS, and mortality risk, among the all possible adjusted model combinations with one to three time-intervals (zero to two time points) based on every time point in 30-day units, the models with the best goodness-of-fit were selected. For mortality analysis, the Kaplan-Meier method with log-rank tests was used to estimate mortality risk according to age group and associations between age groups and death were summarized with hazard ratios and 95% confidence intervals, estimated using the Cox proportional hazards model. For all kinds of statistics, adjustment was applied for following variables: sex, stroke type (ischemic/hemorrhagic), initial National Institutes of Health Stroke Scale score, and stroke risk-factors. Results: Of the 10,636 candidate patients, 7,805 patients met the inclusion criteria and consented to the long-term follow-up. For the analyses of FIM, mRS, and mortality, 5,247, 5,744, and 7,795 patients met the criteria for longitudinal analyses, and were included in the analyses, respectively. In FIM and mRS, the models with the best goodness-of-fit revealed three age groups (โค70, 71-81, and 82โค years old in FIM; โค68, 69-80, and โฅ81 years old in mRS) based on the age of stroke onset, and also did three time-intervals (<90, 90-390, 390< days in FIM; <60, 60-210, and 210< days in mRS) based on post-stroke durations. In mortality, the model for age groups with the best goodness-of-fit revealed three groups (โค66, 67-82, 83โค years old) based on the age of stroke onset, but the best model for post-stroke duration from onset did not divide time-intervals. Conclusion: Based on the 70 and 80 years of age at the onset of stroke, the functional dependence and the risk of death after stroke significantly increased. Age-related differences in functional dependence increased over time after stroke onset; however, the difference in the risk of death according to stroke onset age groups did not increase significantly over time.
์๋ก : ๋ฐ๋ณ ์ฐ๋ น์ ๋ฐ๋ฅธ ๋์กธ์ค ํ ์ํ๋ ๋ง์ ์ฐ๊ตฌ์์ ๋ค๋ฃจ์ด์ ธ ์๋ค. ๊ทธ๋ฌ๋ ์ ํ ์ฐ๊ตฌ๋ค์ ์๋์ ์ผ๋ก ์งง์ ์ด ์ถ์ ๊ธฐ๊ฐ, ์ ์ ์์ ์ฐ๊ตฌ๋์์, ์ ์ ์์ ๋ฐ๋ณตํ๊ฐ๋ผ๋ ํ๊ณ์ ๋ค์ด ์์๋ค. ์ด ์ฐ๊ตฌ์ ๋ชฉ์ ์ ๋๊ท๋ชจ์ ๋ค๊ธฐ๊ด ์ข
๋จ ์ฝํธํธ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ์ฌ ๋์กธ์ค ํ ์ํ๋ฅผ ๊ฒฐ์ ํ๋ ๋์กธ์ค ๋ฐ๋ณ์์ ๊ตฌ์ฒด์ ์ธ ์ฐ๋ น์ ์ ์ํ๊ณ , ๋ฐ๋ณ์ ์ฐ๋ น๊ณผ ๋์กธ์ค ํ ์ฅ๊ธฐ๊ฐ ๊ธฐ๋ฅ์ ์์กด์ฑ ๋ฐ ์ฌ๋ง ์ํ ๊ฐ์ ์ฅ๊ธฐ์ ๊ด๊ณ๋ฅผ ๋ฐํ๋ ๊ฒ์ด์๋ค. ๋ฐฉ๋ฒ: 2012๋
8์๋ถํฐ 2015๋
5์๊น์ง Korean Stroke Cohort for Function and Rehabilitation์ ๋ฑ๋ก๋ ์ด 10,636๋ช
์ ํ์ ์ค ๋์กธ์ค ํ 4๋
์ถ์ ๊ด์ฐฐ์ ์๋ฃํ๊ณ ์ ์ ๊ธฐ์ค์ ์ถฉ์กฑํ ํ์๋ฅผ ๋์์ผ๋ก ๋ถ์ํ์๋ค. ๋ชจ๋ ํ์๋ฅผ FIM(Functional Independent Measure) ๋ฐ mRS(Modified Rankin Scale) ์ ์ ๋ฐ ์ฌ๋ง์ํ๋์์ ์ ์ฌํ ์ํ ๊ถค์ ์ ๊ฐ์ง 1~3๊ฐ ๊ทธ๋ฃน์ผ๋ก ๋ถ๋ฅํ๊ธฐ ์ํด ๊ฐ๋ฅํ ๋ชจ๋ ์กฐํฉ์ผ๋ก ๊ณต๋ณ๋์ ๋ํด ์กฐ์ ๋ ๋ชจ๋ธ๋ค์ ๊ตฌ์ฑํ๋ค. ์ด ์ค ๊ฐ ์ข
์๋ณ์์ ๋ํด ์ ํฉ๋๊ฐ ๊ฐ์ฅ ์ข์ ๋ชจ๋ธ๋ค์ ์ ํํ๋ค. ์ดํ, ๊ทธ๋ฐ ๋ค์, FIM, mRS ๋ฐ ์ฌ๋ง ์ํ์ ๋ํ ์ฐ๋ น ๊ทธ๋ฃน์ ์ํฅ์ธก๋ฉด์์ ๊ฐ์ฅ ๊ธ๊ฒฉํ ๋ณํ๊ฐ ์๋ ์์ ์ ์กฐ์ฌํ๊ธฐ ์ํด 1-3 ์๊ฐ ๊ฐ๊ฒฉ(0-2 ์์ )์ ๊ธฐ๋ฐ์ผ๋ก ํ๋ ๋ชจ๋ ๊ฐ๋ฅํ ์์ ๋ชจ๋ธ ์กฐํฉ ์ค 30์ผ ๋จ์์ ๋ชจ๋ ์์ ์์ ์ ํฉ๋๊ฐ ๊ฐ์ฅ ์ข์ ๋ชจ๋ธ์ ์ ํํ๋ค. ์ฌ๋ง์ํ ๋ถ์์ ์ํด ๋ก๊ทธ ์์ ๊ฒ์ ์ ํฌํจํ๋ Kaplan-Meier ๋ฐฉ๋ฒ์ ์ฌ์ฉํ์ฌ ์ฐ๋ น ๊ทธ๋ฃน์ ๋ฐ๋ฅธ ์ฌ๋ง ์ํ๋๋ฅผ ์ถ์ ํ๊ณ ์ฐ๋ น ๊ทธ๋ฃน๊ณผ ์ฌ๋ง ๊ฐ์ ์ฐ๊ด์ฑ์ Cox ๋น๋ก ์ํ ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ์ถ์ ์ํ๋น ๋ฐ 95% ์ ๋ขฐ ๊ตฌ๊ฐ์ผ๋ก ์์ฝํ๋ค. ๋ชจ๋ ์ข
๋ฅ์ ํต๊ณ์ ๋ํด ์ฑ๋ณ, ๋์กธ์ค ์ ํ(ํํ์ฑ/์ถํ์ฑ), ์ด๊ธฐ National Institutes of Health Stroke Scale ์ ์ ๋ฐ ๋์กธ์ค ์ํ ์ธ์๋ค์ ๋ํด ์กฐ์ ์ด ์ ์ฉ๋์๋ค. ๊ฒฐ๊ณผ: 10,636๋ช
์ ํ๋ณด ํ์ ์ค 7,805๋ช
์ ํ์๊ฐ ํฌํจ ๊ธฐ์ค์ ์ถฉ์กฑํ๊ณ ์ฅ๊ธฐ ์ถ์ ๊ด์ฐฐ์ ๋์ํ๋ค. FIM, mRS, ์ฌ๋ง๋ฅ ๋ถ์์ ์ํด 5,247๋ช
, 5,744๋ช
, 7,795๋ช
์ ํ์๊ฐ ์ข
๋จ ๋ถ์์ ๊ธฐ์ค์ ์ถฉ์กฑํ์ฌ ๊ฐ ๋ถ์์ ํฌํจ๋์๋ค. FIM๊ณผ mRS์์ ๊ฐ์ฅ ์ข์ ์ ํฉ๋๋ฅผ ๊ฐ์ง ๋ชจ๋ธ์ ๋์กธ์ค ๋ฐ๋ณ ์ฐ๋ น์ ๋ฐ๋ผ ์ธ ๊ฐ์ง ์ฐ๋ น ๊ทธ๋ฃน(FIM์์ โค70์ธ, 71-81์ธ ๋ฐ 82์ธโค; mRS์์ โค68์ธ, 69-80์ธ ๋ฐ 81์ธโค), ๋์กธ์ค ํ ๊ธฐ๊ฐ์ ๋ฐ๋ผ ์ธ ๊ตฌ๊ฐ์ ์๊ฐ ๊ฐ๊ฒฉ (FIM์์ <90์ผ, 90-390์ผ, 390์ผ<, mRS์์ <60์ผ, 60-210์ผ ๋ฐ 210์ผ<) ์ ๋ฐํ๋๋ค. ์ฌ๋ง๋ฅ ์์ ์ฐ๋ น ๊ทธ๋ฃน์ ๋ํ ๊ฐ์ฅ ์ ํฉ๋๊ฐ ๊ฐ์ฅ ์ข์ ๋ชจ๋ธ์ ๋์กธ์ค ๋ฐ๋ณ ์ฐ๋ น์ ๊ธฐ์ค์ผ๋ก ์ธ ๊ทธ๋ฃน (โค66์ธ, 67-82์ธ, 83์ธ โค)์ ๋ํ๋์ง๋ง, ๋์กธ์ค ๋ฐ๋ณ ์ดํ ๊ธฐ๊ฐ์ ๋ํ ๊ฐ์ฅ ์ ํฉ๋๊ฐ ์ข์ ๋ชจ๋ธ์ ์๊ฐ ๊ฐ๊ฒฉ์ ๋๋์ง ์์๋ค. ๊ฒฐ๋ก : ๋์กธ์ค ๋ฐ๋ณ ์ ์ฝ 70์ธ ๋ฐ 80์ธ ์ฐ๋ น์ ๊ธฐ์ค์ผ๋ก ๊ธฐ๋ฅ์ ์์กด์ฑ๊ณผ ๋์กธ์ค ํ ์ฌ๋ง ์ํ์ด ์ ์๋ฏธํ๊ฒ ์ฆ๊ฐํ๋ค. ๊ธฐ๋ฅ์ ์์กด์ฑ์ ๋ฐ๋ณ ์ฐ๋ น์ ๋ฐ๋ฅธ ์ฐจ์ด๋ ๋์กธ์ค ๋ฐ๋ณ ํ ์๊ฐ์ด ์ง๋จ์ ๋ฐ๋ผ ์ฆ๊ฐํ์ง๋ง, ๋์กธ์ค ๋ฐ๋ณ ์ฐ๋ น๊ตฐ์ ๋ฐ๋ฅธ ์ฌ๋ง ์ํ์ ์ฐ๋ น๊ตฐ๊ฐ ์ฐจ์ด๋ ์๊ฐ์ด ์ง๋จ์ ๋ฐ๋ผ ์ ์๋ฏธํ๊ฒ ์ฆ๊ฐํ์ง ์์๋ค.open์
๋ฅ๋ ฅ๋ชจ์ ์ถ์ ์น๋ฅผ ์ด์ฉํ ํ๊ตํจ๊ณผ์ฑ ๋ถ์๋ฐฉ๋ฒ์ ๊ดํ ์ฐ๊ตฌ : ์๊ณ์ ์ ํ๋ชจํ์ ์ ์ฉ์ ์ค์ฌ์ผ๋ก
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ต์กํ๊ณผ ๊ต์ก๋ฐฉ๋ฒ์ ๊ณต,2000.Maste
Classification of Phonocardiogram Recordings Using Vision Transformer Architecture
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ง๋ฅ์ ๋ณด์ตํฉํ๊ณผ, 2023. 2. ์๋ด์.์ฌ์ฅ ์งํ์ ์ง๋จ์ ์ค์ํ ์ํ์ ์ ๋ณด์ธ ์ฌ์ฅ์ ์ํ ๋ฐ ๊ธฐ๋ฅ์ ๋ํ ์ ๋ณด๋ฅผ ์ ๊ณตํ๋ ์ฌ์(heart sound)์ ์ธก์ ํ๋ ๊ฒ์ผ๋ก๋ถํฐ ์์๋๋ค. ์ฒญ์ง๊ธฐ๋ฅผ ํตํด ์ฌ์์ ๋ฃ๊ณ ์ง๋จํ๋ ๊ฒ์ ์ ๋ฐ๊ฒ์ฌ์ ๋นํด ์ ํ๋๊ฐ ๋จ์ด์ง์ง๋ง, ์ฉ์ดํ๊ณ ๋น์ฉ์ด ๊ฑฐ์ ๋ค์ง ์๊ธฐ ๋๋ฌธ์ ์ง๋จ์ ์์ด ํ์์ ์ผ๋ก ์ฌ์ฉ๋๋ค. ๊ฑด๊ฐํ ์ฌ๋์๊ฒ์๋ ๊ท์น์ ์ด๊ณ ๋ช
ํํ ์ฌ์ฅ ๋ฐ๋ ์๋ฆฌ๊ฐ ๋ค๋ฆฌ์ง๋ง, ๊ทธ๋ ์ง ์์ ์ฌ๋์๊ฒ์๋ ์ฌ์ฅ ์๋ฆฌ์ ํจ๊ป ์ก์์ด ํจ๊ป ๋ค๋ฆฌ๊ธฐ๋ ํ๋ค. ์ด๋ฌํ ์ก์์ ์ฌ์ก์(heart murmur) ์ด๋ผ๊ณ ํ๋ฉฐ, ์ฌ์ก์์ ํน์ง๊ณผ ์ก์์ด ๋ค๋ฆฌ๋ ์์น ๋ฑ์ ์ฌ์ฉํ์ฌ ์ฌ์ฅ๋ณ์ ํ๋จํ ์ ์๋ค. ์ฌ์ ๋ฐ์ดํฐ๋ฅผ ๋
น์ํด์ ๋ง๋ค์ด์ง ์ฌ์๋(PCG; Phonocardiograms) ๋ฐ์ดํฐ๋ก ์ด ์ฌ๋์ด ์ฌ์ฅ๋ณ ํ์์ธ์ง ์ ๋ฌด๋ฅผ ํ์งํ๊ฑฐ๋, ์ฌ์ฅ ์๋ฆฌ์ ์ด์์ด ์๋์ง ๋ฑ์ ํ์งํ ์ ์๋ค. ์ฌ์์์ ๋น์ ์์ ์ธ ์ฌ์ฅ ๊ธฐ๋ฅ์ ๊ฐ์งํ๊ธฐ ์ํ ์๋ํ๋ ๋ชจ๋ธ์ ๊ฐ๋ฐํ๋ ๊ฒ์ ๊ฐ๋ฐ๋์๊ตญ๊ณผ ๊ฐ์ด ์ ๋ฌธ๊ฐ์ ์๋ณธ์ด ๋ถ์กฑํ ๋๋ผ์ ์ฌ์ฅ๋ณ์ผ๋ก ๊ณ ํต๋ฐ๋ ์ฌ๋๋ค์๊ฒ ์ค์ํ ์ฐ๊ตฌ ์ฃผ์ ์ด๋ค.
์๋ฃ๋ฐ์ดํฐ๋ฅผ ๋ชจ๋ธ๋ง ํ๋ ๋ฐฉ๋ฒ์๋ ๊ณ ์ ์ ์ธ ๊ธฐ๊ณํ์ต(Machine Learning) ๊ธฐ๋ฐ์ ๋ชจ๋ธ์ ํ์ฉํ๋ ๋ฐฉ๋ฒ๊ณผ, ๋ฅ๋ฌ๋(Deep Learning) ๊ธฐ๋ฐ์ ๋ชจ๋ธ์ ํ์ฉํ๋ ๋ฐฉ๋ฒ์ผ๋ก ๋๋ ์ ์๋ค. ๊ธฐ๊ณํ์ต ๊ธฐ๋ฐ์ ์๋ฃ๋ฐ์ดํฐ ๋ชจ๋ธ๋ง์ ํน์ฑ(feature)์ ์ง์ ์ถ์ถํด์ผ ํ๋ฏ๋ก, ์ฌ์ฉํ๋ ๋ฐ์ดํฐ์ ๋ํด ์ฐ๊ตฌ์์ ์ฌ์ ์ง์๊ณผ ์ ์ฒ๋ฆฌ(pre-processing) ๋ฐฉ๋ฒ๋ค์ด ํฐ ์ํฅ์ ๋ฏธ์น๋ค. ๋ฐ๋ฉด ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ์ ๋ชจ๋ธ์ ์ด๋ฌํ ํน์ฑ๊น์ง๋ ๋ชจ๋ธ์ด ์ง์ ํ์ตํ์ฌ ์ถ์ถํ๊ธฐ ๋๋ฌธ์, ์ฌ์ฉํ๋ ๋ฐ์ดํฐ์ ๋ํ ์ฐ๊ตฌ์์ ์ฌ์ ์ง์๊ณผ ์ ์ฒ๋ฆฌ ๋ฐฉ๋ฒ์ ์ํฅ์ด ๋น๊ต์ ๋ฎ๋ค. ์ ํต์ ์ผ๋ก ์๋ฃ๋ฐ์ดํฐ ๋ชจ๋ธ๋ง์๋ ๊ธฐ๊ณํ์ต ๋ชจ๋ธ๋ค์ด ๋ง์ด ์ฌ์ฉ๋์์ผ๋, ์ต๊ทผ ์๋ฃ๋ฐ์ดํฐ ๋ถ์ผ์์๋ ๋ฅ๋ฌ๋ ๋ถ์ผ์์ ์ข์ ์ฑ๋ฅ์ ๋ณด์ด๋ ๋ชจ๋ธ์ ์ฌ์ฉํด์ ๊ธฐ์กด ์๋ฃ๋ฐ์ดํฐ ๋ชจ๋ธ๋ง์์ ์ฑ๋ฅ์ ๋์ด๋ ์ฐ๊ตฌ๋ค์ด ์งํ๋๊ณ ์์ผ๋ฉฐ, ๊ณ ์ ์ ์ธ ๊ธฐ๊ณํ์ต๋ค๋ณด๋ค ๋ ์ข์ ์ฑ๋ฅ์ ๋ณด์ธ๋ค. ํ์ง๋ง ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ์ ๋ชจ๋ธ๋ค์ ๋๋ถ๋ถ ๊ฒฐ๊ณผ์ ๋ํ ํด์์ ํ ์ ์์ด, ์ ๋ฌธ๊ฐ์ ์ง๋จ์ ๋์์ ์ฃผ๋ ๊ฒ์ ์ด๋ ค์์ ๊ฒช๊ณ ์๋ค.
๋ณธ ๋
ผ๋ฌธ์์ ์ฌ์ฉ๋๋ Vision Transformer ๊ตฌ์กฐ์ ๊ฒฝ์ฐ์๋ ์
ํ ์ดํ
์
(self-attention)์ด ํฌํจ๋์ด ์์ผ๋ฉฐ, ์ด๋ ๊ฒฐ๊ณผ๋ฅผ ์ดํดํ๋ ๋ฐ ๋์์ด ๋๋ ์ดํ
์
์ ์(attention score)๋ฅผ ๊ณ์ฐํ์ฌ ์ด๋ฅผ ์ดํ
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๋ง์คํฌ(attention mask)๋ก ์๊ฐํ ํ ์ ์๋ค๋ ์ฅ์ ๊ณผ, ์ปดํจํฐ ๋น์ (Computer Vision)๋ถ์ผ์ ์ด๋ฏธ์ง ๋ถ๋ฅ(Image Classification) ํ์คํฌ(task)์์ ๋์ ์ฑ๋ฅ์ ๋ณด์ด๋ ์ฅ์ ์ ๊ฐ์ง ๋ชจ๋ธ์ด๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์ฌ๋ฌ ์ฒญ์ง ์์น์์ ์ธก์ ๋ ์ฌ์ฅ ์๋ฆฌ ๋
น์์์ ์ก์์ ์ ๋ฌด์ ์์ ๊ฒฐ๊ณผ๋ฅผ ์ ๊ฐ์งํ๋ ๋ชจ๋ธ์ ์ ์ํ๋ค. ๋ณด๋ค ๋์ ๋ถ๋ฅ ์ฑ๋ฅ๊ณผ ๊ฒฐ๊ณผ์ ํด์์ ๋์์ ์ฃผ๊ธฐ ์ํด ์๊ฐ์ ์ธ ์ ๊ทผ ๋ฐฉ์์ ์ฌ์ฉํ๋ค. ์ ์๋ ๋ชจ๋ธ์ ์ ํธ์ ๋ฆฌ์ํ๋ง(resampling)์ด๋ ํํฐ๋ง(filtering) ์์ด ์ฌ์ฅ ์๋ฆฌ ์ ํธ๋ฅผ ์คํํธ๋ก๊ทธ๋จ(spectrogram)์ผ๋ก ๋ณํํ๊ณ , ๋ณํ๋ ์ด๋ฏธ์ง๋ฅผ ํ์์ ์ธ๊ตฌํต๊ณํ์ ์ ๋ณด์ ํจ๊ป ์
๋ ฅ ๋ฐ์ ์ฌ์ก์๊ณผ ์์ ๊ฒฐ๊ณผ๋ฅผ ์ถ๋ก (inference)ํ๋ค. ์์ ๊ฒฐ๊ณผ ์๋ณ ์์
์ ๊ฒฝ์ฐ ํ
์คํธ ๋ฐ์ดํฐ์ ๋ํ ๋ํ ๋น์ฉ ํจ์ ์ ์ 11943์, ์ฌ์ก์ ๋ถ๋ฅ์ ๋ํด์๋ 0.69์ ๊ฐ์ค์น ์ ํ๋๋ฅผ ๊ธฐ๋กํ๋ค. ๋ํ ๋ชจ๋ธ์๋ ์ด๋ ๋ถ๋ถ์ ๋ณด๊ณ ํ๋จํ๋์ง ์๊ฐํ๊ฐ ๊ฐ๋ฅํ ์ดํ
์
๋ง์คํฌ๊ฐ ํฌํจ๋์ด ์๋ค.Diagnosis of cardiac disease starts with measuring heart sound. It provides information about the cardiac condition and function, which is important medical information. Diagnosis by listening to a heart sound through a stethoscope is less accurate than a detailed examination, but it is essential for diagnosis because it is easy and inexpensive. A healthy person can hear a regular and clear heartbeat, but in an unhealthy person, a murmur can be heard along with the heartbeat. Such a noise is called a heart murmur, and if the characteristics of the heart murmur and the location at which the noise is heard are used, heart disease can be judged by this alone. Phonocardiograms (PCG) data created by recording heart sound data can detect whether a person has a heart disease, detect abnormal heart sounds, etc. Developing models is an important research topic for people suffering from heart disease in countries with limited expertise and capital, such as developing countries.
The method of modeling medical data can be divided into a method using a classical machine learning-based model and a method using a deep learning-based model. Since machine learning-based medical data modeling requires the direct extraction of features, the researcher's prior knowledge and pre-processing methods have a great influence on the data used. On the other hand, in deep learning-based models, the influence of the researcher's prior knowledge and preprocessing method on the data used is relatively low because the model learns and extracts even these characteristics. Traditionally, many machine learning models have been used for medical data modeling, but recently, in the medical data field, studies are underway to improve the performance of existing medical data modeling by using a model with good performance in the deep learning field. shows better performance than However, most deep learning-based models cannot interpret the results, so it is difficult to help experts in diagnosis.
In the case of the Vision Transformer structure used in this paper, self-attention is included, which calculates an attention score that helps to understand the result and converts it into an attention mask, and it is a model with the advantage of showing high performance in the image classification task in the computer vision field.
In this paper, proposing a model that detects the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes from phonocardiogram well. A visual approach was used to aid in the interpretation of results and higher classification performance. The proposed model converts heart sound signals into spectrograms without requiring resampling or signal filtering, and infers cardiac noise and clinical outcomes by receiving the image with the patient's demographic information. For the clinical outcome identification task on the test data, it shows a Challenge cost score of 11943. The result shows a weighted accuracy score of 0.69 for the murmur detection classification on the test data. In addition, the model includes an attention mask that allows visualization of which part was viewed and judged.์ 1 ์ฅ ์ ๋ก 1
์ 1 ์ ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ 1
์ 2 ์ ์ฐ๊ตฌ์ ๋ด์ฉ 4
์ 2 ์ฅ ์ ํ ์ฐ๊ตฌ 7
์ 1 ์ ์ฌ์ฅ ์๋ฆฌ ๋ถ๋ฅ 7
์ 2 ์ ์๋ฆฌ์ ์๊ฐํ 12
์ 3 ์ Vision Transformer 16
์ 4 ์ ๋ฐ์ดํฐ ์ฆ๊ฐ 22
์ 3 ์ฅ ์ฐ๊ตฌ ๋ฐฉ๋ฒ ๋ฐ ์ค๊ณ 28
์ 1 ์ ๋ฐ์ดํฐ์
28
์ 2 ์ ์ ์ฒ๋ฆฌ ๋ฐ ์ด๋ฏธ์ง ๋ฐ์ดํฐ์
์์ฑ 32
์ 3 ์ ๋ชจ๋ธ ํ์ต 37
์ 4 ์ฅ ์คํ ๋ฐ ๊ฒฐ๊ณผ 41
์ 1 ์ ํ๊ฐ ์งํ 41
์ 2 ์ ํ๋ จ ๋ฐ์ดํฐ์ ๋ํ ์ ์ ์ฐ๊ตฌ 43
์ 3 ์ ๋ํ ๋ฐ์ดํฐ์
์ ๋ํ ์คํ ๊ฒฐ๊ณผ 47
์ 5 ์ฅ ๋
ผ์ 50
์ 1 ์ ๋ํ ๊ฒฐ๊ณผ์ ๋ํ ๊ณ ์ฐฐ 50
์ 2 ์ ์ดํ
์
๋ง์คํฌ 52
์ 3 ์ ํฅํ ๊ณํ 56
์ 4 ์ ํ๊ณ 58
์ 6 ์ฅ ๊ฒฐ๋ก 59
์ฐธ๊ณ ๋ฌธํ 61
Abstract 68์