23 research outputs found

    Characteristics and Risk Factors of Aspiration in Lateral Medullary Infarction

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    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๏ผœ0.05). 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๏ผœ0.05). 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

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    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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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2013. 2. ๊น€์†Œ์˜.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„œ ๊ตญ์†Œ๋‡Œํ—ˆํ˜ˆ ๋ชจ๋ธ์—์„œ ์ €์ฒด์˜จ์š”๋ฒ•์ด NLRP3 ์ธํ”Œ๋ผ๋งˆ์ข€ ๋ฐœํ˜„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2015. 2. ์œค๋ณ‘์šฐ.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 Spragueโ€“Dawley (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

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    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

    ์ž„ํ”Œ๋ž€ํŠธ ์ˆ˜์ˆ  ํ›„ ๋ฐœ์ƒํ•œ ํ•˜์น˜์กฐ์‹ ๊ฒฝ ์†์ƒ์˜ ํšŒ๋ณต์–‘์ƒ์— ๋Œ€ํ•œ ํ›„ํ–ฅ์  ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ์น˜์˜ํ•™๋Œ€ํ•™์› : ์น˜์˜ํ•™๊ณผ, 2014. 2. ์ด์ข…ํ˜ธ.๋ณธ ์—ฐ๊ตฌ๋Š” ์น˜๊ณผ ์ž„ํ”Œ๋ž€ํŠธ ์ˆ˜์ˆ  ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•œ ํ•˜์น˜์กฐ์‹ ๊ฒฝ์†์ƒ ํ™˜์ž๋“ค์— ๋Œ€ํ•œ ํ›„ํ–ฅ์  ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ™˜์ž๋“ค์— ๋Œ€ํ•œ ์ผ๋ฐ˜์ ์ธ ์ž„์ƒ์ ์ธ ๊ฒฝํ–ฅ์„ ํŒŒ์•…ํ•˜๊ณ  ํ•˜์น˜์กฐ์‹ ๊ฒฝ์†์ƒ ์‹œ ์–ด๋–ป๊ฒŒ ๋Œ€์ฒ˜ํ•˜๋Š” ๊ฒƒ์ด ์‹ค์ œ๋กœ ํšจ๊ณผ๊ฐ€ ์žˆ๋Š”์ง€ ์•Œ์•„๋ณด๋Š” ๊ฒƒ์„ ๊ทธ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. 2008๋…„ 1์›”๋ถ€ํ„ฐ 2011๋…„ 1์›”๊นŒ์ง€ ์ž„ํ”Œ๋ž€ํŠธ ์ˆ˜์ˆ  ํ›„ ํ•˜์น˜์กฐ์‹ ๊ฒฝ์†์ƒ์„ ์ฃผ์†Œ๋กœ ์„œ์šธ๋Œ€ํ•™๊ต์น˜๊ณผ๋ณ‘์›์— ๋‚ด์›ํ•œ 113๋ช…์˜ ํ™˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ›„ํ–ฅ์ ์œผ๋กœ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. 113๋ช…์— ๋Œ€ํ•œ ํ™˜์ž๋“ค์˜ ์ฐจํŠธ์™€ ๋ฐฉ์‚ฌ์„  ์˜์ƒ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์„ฑ๋ณ„, ๋‚˜์ด, ์‹ ๊ฒฝ์†์ƒ์„ ์ผ์œผํ‚จ ์ž„ํ”Œ๋ž€ํŠธ์˜ ์‹๋ฆฝ๋ถ€์œ„, ์ž„ํ”Œ๋ž€ํŠธ์˜ ํ›„ ์ฒ˜์น˜(์ž„ํ”Œ๋ž€ํŠธ ์ œ๊ฑฐ, ์งง๊ฒŒ ํ•˜๊ธฐ, ์ƒํƒœ ์œ ์ง€), ๋ฐฉ์‚ฌ์„  ์˜์ƒ์—์„œ ์ž„ํ”Œ๋ž€ํŠธ์™€ ํ•˜์น˜์กฐ์‹ ๊ฒฝ ๊ฐ„์˜ ๊ฑฐ๋ฆฌ, ๋‚ด์› ์ „ ์ฒ˜์น˜, ์ดˆ๊ธฐ์†์ƒ ํ›„ ์„œ์šธ๋Œ€ํ•™๊ต์น˜๊ณผ๋ณ‘์›์— ๋‚ด์›ํ•˜๊ธฐ ๊นŒ์ง€ ๊ฑธ๋ฆฐ ์‹œ๊ฐ„์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ๋ชจ์•„ ์ด๋Ÿฌํ•œ ์š”์†Œ๋“ค์ด ํ™˜์ž์˜ ํšŒ๋ณต๊ณผ ์–ด๋–ป๊ฒŒ ์—ฐ๊ด€์„ฑ์ด ์žˆ๋Š”์ง€ ์•Œ์•„๋ณด์•˜๋‹ค. ํ™˜์ž์˜ ํšŒ๋ณต์ •๋„๋Š” ๊ฐ๊ฐ ํ™˜์ž๊ฐ€ ์ฃผ๊ด€์ ์œผ๋กœ ํ˜ธ์†Œํ•œ ๋‚ด์šฉ๊ณผ ์ž„์ƒ์ ์œผ๋กœ ์‹ค์‹œํ•œ ๊ฐ๊ด€์ ์ธ ํ‰๊ฐ€์— ๊ทผ๊ฑฐํ•˜์—ฌ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ž„ํ”Œ๋ž€ํŠธ ์ˆ˜์ˆ  ์ค‘ ํ•˜์ง€์ดˆ์‹ ๊ฒฝ ์†์ƒ์ด ๋ฐœ์ƒํ•˜๋Š” ๋น„์œจ์€ ๋‚จ์ž๋ณด๋‹ค ์—ฌ์ž๊ฐ€ 2๋ฐฐ ์ •๋„๋กœ ๋†’์•˜์œผ๋ฉฐ ์—ฐ๋ น๋ณ„๋กœ๋Š” 50๋Œ€์™€ 60๋Œ€๊ฐ€ ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ•˜์•… ์ œ2๋Œ€๊ตฌ์น˜ ๋ถ€์œ„์— ์ž„ํ”Œ๋ž€ํŠธ๋ฅผ ์‹๋ฆฝํ•˜๋Š” ๊ฒฝ์šฐ์— ํ•˜์น˜์กฐ์‹ ๊ฒฝ์†์ƒ์ด ๊ฐ€์žฅ ํ˜ธ๋ฐœํ•˜์˜€๋‹ค. ์†์ƒ๋œ ํ•˜์น˜์กฐ์‹ ๊ฒฝ์˜ ํšŒ๋ณต์€ ์ž„ํ”Œ๋ž€ํŠธ๋ฅผ ์ œ๊ฑฐํ•˜๊ฑฐ๋‚˜ ์งง๊ฒŒ ํ•˜๋Š” ์ฒ˜๋ฆฌ์™€ ํ•˜์น˜์กฐ์‹ ๊ฒฝ์†์ƒ์ด ๋ฐœ์ƒํ•œ ์ดํ›„ ์„œ์šธ๋Œ€ํ•™๊ต์น˜๊ณผ๋ณ‘์›์œผ๋กœ ๋‚ด์›ํ•˜๋Š”๋ฐ ๊นŒ์ง€ ๊ฑธ๋ฆฌ๋Š” ๊ธฐ๊ฐ„์€ ์œ ์˜๋ฏธํ•œ ์—ฐ๊ด€์„ฑ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค.I. ์„œ๋ก  6 II. ํ™˜์ž ๋ฐ ๋ฐฉ๋ฒ• 8 1. ํ™˜์ž 8 2. ํ†ต๊ณ„ 9 III. ๊ฒฐ๊ณผ 10 IV. ๊ณ ์ฐฐ 13 V. ๊ฒฐ๋ก  15 ์ฐธ๊ณ ๋ฌธํ—Œ 16Maste

    Analysis on the Luminance Degradation of Organic Light-Emitting Diodes Doped with Fluorescent Dyes

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ. ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2010.2.Maste

    ๋‡Œ์กธ์ค‘ ํ›„ ๊ธฐ๋Šฅ์  ์˜์กด์„ฑ ๋ฐ ์‚ฌ๋ง๋ฅ : ์—ฐ๋ น๋ณ„ ์žฅ๊ธฐ ์˜ˆํ›„

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    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 (โ‰ค70, 71-81, and 82โ‰ค years old in FIM; โ‰ค68, 69-80, and โ‰ฅ81 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 (โ‰ค66, 67-82, 83โ‰ค 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. ์„œ๋ก : ๋ฐœ๋ณ‘ ์—ฐ๋ น์— ๋”ฐ๋ฅธ ๋‡Œ์กธ์ค‘ ํ›„ ์˜ˆํ›„๋Š” ๋งŽ์€ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฃจ์–ด์ ธ ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์„ ํ–‰ ์—ฐ๊ตฌ๋“ค์€ ์ƒ๋Œ€์ ์œผ๋กœ ์งง์€ ์ด ์ถ”์  ๊ธฐ๊ฐ„, ์ ์€ ์ˆ˜์˜ ์—ฐ๊ตฌ๋Œ€์ƒ์ž, ์ ์€ ์ˆ˜์˜ ๋ฐ˜๋ณตํ‰๊ฐ€๋ผ๋Š” ํ•œ๊ณ„์ ๋“ค์ด ์žˆ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋Œ€๊ทœ๋ชจ์˜ ๋‹ค๊ธฐ๊ด€ ์ข…๋‹จ ์ฝ”ํ˜ธํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋‡Œ์กธ์ค‘ ํ›„ ์˜ˆํ›„๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘์‹œ์˜ ๊ตฌ์ฒด์ ์ธ ์—ฐ๋ น์„ ์ •์˜ํ•˜๊ณ , ๋ฐœ๋ณ‘์‹œ ์—ฐ๋ น๊ณผ ๋‡Œ์กธ์ค‘ ํ›„ ์žฅ๊ธฐ๊ฐ„ ๊ธฐ๋Šฅ์  ์˜์กด์„ฑ ๋ฐ ์‚ฌ๋ง ์œ„ํ—˜ ๊ฐ„์˜ ์žฅ๊ธฐ์  ๊ด€๊ณ„๋ฅผ ๋ฐํžˆ๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๋ฐฉ๋ฒ•: 2012๋…„ 8์›”๋ถ€ํ„ฐ 2015๋…„ 5์›”๊นŒ์ง€ Korean Stroke Cohort for Function and Rehabilitation์— ๋“ฑ๋ก๋œ ์ด 10,636๋ช…์˜ ํ™˜์ž ์ค‘ ๋‡Œ์กธ์ค‘ ํ›„ 4๋…„ ์ถ”์  ๊ด€์ฐฐ์„ ์™„๋ฃŒํ•˜๊ณ  ์„ ์ • ๊ธฐ์ค€์„ ์ถฉ์กฑํ•œ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ชจ๋“  ํ™˜์ž๋ฅผ FIM(Functional Independent Measure) ๋ฐ mRS(Modified Rankin Scale) ์ ์ˆ˜ ๋ฐ ์‚ฌ๋ง์œ„ํ—˜๋„์—์„œ ์œ ์‚ฌํ•œ ์˜ˆํ›„ ๊ถค์ ์„ ๊ฐ€์ง„ 1~3๊ฐœ ๊ทธ๋ฃน์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ€๋Šฅํ•œ ๋ชจ๋“  ์กฐํ•ฉ์œผ๋กœ ๊ณต๋ณ€๋Ÿ‰์— ๋Œ€ํ•ด ์กฐ์ •๋œ ๋ชจ๋ธ๋“ค์„ ๊ตฌ์„ฑํ–ˆ๋‹ค. ์ด ์ค‘ ๊ฐ ์ข…์†๋ณ€์ˆ˜์— ๋Œ€ํ•ด ์ ํ•ฉ๋„๊ฐ€ ๊ฐ€์žฅ ์ข‹์€ ๋ชจ๋ธ๋“ค์„ ์„ ํƒํ–ˆ๋‹ค. ์ดํ›„, ๊ทธ๋Ÿฐ ๋‹ค์Œ, FIM, mRS ๋ฐ ์‚ฌ๋ง ์œ„ํ—˜์— ๋Œ€ํ•œ ์—ฐ๋ น ๊ทธ๋ฃน์˜ ์˜ํ–ฅ์ธก๋ฉด์—์„œ ๊ฐ€์žฅ ๊ธ‰๊ฒฉํ•œ ๋ณ€ํ™”๊ฐ€ ์žˆ๋Š” ์‹œ์ ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด 1-3 ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ(0-2 ์‹œ์ )์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๋ชจ๋“  ๊ฐ€๋Šฅํ•œ ์ˆ˜์ • ๋ชจ๋ธ ์กฐํ•ฉ ์ค‘ 30์ผ ๋‹จ์œ„์˜ ๋ชจ๋“  ์‹œ์ ์—์„œ ์ ํ•ฉ๋„๊ฐ€ ๊ฐ€์žฅ ์ข‹์€ ๋ชจ๋ธ์„ ์„ ํƒํ–ˆ๋‹ค. ์‚ฌ๋ง์œ„ํ—˜ ๋ถ„์„์„ ์œ„ํ•ด ๋กœ๊ทธ ์ˆœ์œ„ ๊ฒ€์ •์„ ํฌํ•จํ•˜๋Š” Kaplan-Meier ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์—ฐ๋ น ๊ทธ๋ฃน์— ๋”ฐ๋ฅธ ์‚ฌ๋ง ์œ„ํ—˜๋„๋ฅผ ์ถ”์ •ํ•˜๊ณ  ์—ฐ๋ น ๊ทธ๋ฃน๊ณผ ์‚ฌ๋ง ๊ฐ„์˜ ์—ฐ๊ด€์„ฑ์„ Cox ๋น„๋ก€ ์œ„ํ—˜ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”์ • ์œ„ํ—˜๋น„ ๋ฐ 95% ์‹ ๋ขฐ ๊ตฌ๊ฐ„์œผ๋กœ ์š”์•ฝํ–ˆ๋‹ค. ๋ชจ๋“  ์ข…๋ฅ˜์˜ ํ†ต๊ณ„์— ๋Œ€ํ•ด ์„ฑ๋ณ„, ๋‡Œ์กธ์ค‘ ์œ ํ˜•(ํ—ˆํ˜ˆ์„ฑ/์ถœํ˜ˆ์„ฑ), ์ดˆ๊ธฐ National Institutes of Health Stroke Scale ์ ์ˆ˜ ๋ฐ ๋‡Œ์กธ์ค‘ ์œ„ํ—˜ ์ธ์ž๋“ค์— ๋Œ€ํ•ด ์กฐ์ •์ด ์ ์šฉ๋˜์—ˆ๋‹ค. ๊ฒฐ๊ณผ: 10,636๋ช…์˜ ํ›„๋ณด ํ™˜์ž ์ค‘ 7,805๋ช…์˜ ํ™˜์ž๊ฐ€ ํฌํ•จ ๊ธฐ์ค€์„ ์ถฉ์กฑํ•˜๊ณ  ์žฅ๊ธฐ ์ถ”์  ๊ด€์ฐฐ์— ๋™์˜ํ–ˆ๋‹ค. FIM, mRS, ์‚ฌ๋ง๋ฅ  ๋ถ„์„์„ ์œ„ํ•ด 5,247๋ช…, 5,744๋ช…, 7,795๋ช…์˜ ํ™˜์ž๊ฐ€ ์ข…๋‹จ ๋ถ„์„์˜ ๊ธฐ์ค€์„ ์ถฉ์กฑํ•˜์—ฌ ๊ฐ ๋ถ„์„์— ํฌํ•จ๋˜์—ˆ๋‹ค. FIM๊ณผ mRS์—์„œ ๊ฐ€์žฅ ์ข‹์€ ์ ํ•ฉ๋„๋ฅผ ๊ฐ€์ง„ ๋ชจ๋ธ์€ ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘ ์—ฐ๋ น์— ๋”ฐ๋ผ ์„ธ ๊ฐ€์ง€ ์—ฐ๋ น ๊ทธ๋ฃน(FIM์—์„œ โ‰ค70์„ธ, 71-81์„ธ ๋ฐ 82์„ธโ‰ค; mRS์—์„œ โ‰ค68์„ธ, 69-80์„ธ ๋ฐ 81์„ธโ‰ค), ๋‡Œ์กธ์ค‘ ํ›„ ๊ธฐ๊ฐ„์— ๋”ฐ๋ผ ์„ธ ๊ตฌ๊ฐ„์˜ ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ (FIM์—์„œ <90์ผ, 90-390์ผ, 390์ผ<, mRS์—์„œ <60์ผ, 60-210์ผ ๋ฐ 210์ผ<) ์„ ๋ฐํ˜€๋ƒˆ๋‹ค. ์‚ฌ๋ง๋ฅ ์—์„œ ์—ฐ๋ น ๊ทธ๋ฃน์— ๋Œ€ํ•œ ๊ฐ€์žฅ ์ ํ•ฉ๋„๊ฐ€ ๊ฐ€์žฅ ์ข‹์€ ๋ชจ๋ธ์€ ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘ ์—ฐ๋ น์„ ๊ธฐ์ค€์œผ๋กœ ์„ธ ๊ทธ๋ฃน (โ‰ค66์„ธ, 67-82์„ธ, 83์„ธ โ‰ค)์„ ๋‚˜ํƒ€๋ƒˆ์ง€๋งŒ, ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘ ์ดํ›„ ๊ธฐ๊ฐ„์— ๋Œ€ํ•œ ๊ฐ€์žฅ ์ ํ•ฉ๋„๊ฐ€ ์ข‹์€ ๋ชจ๋ธ์€ ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ์„ ๋‚˜๋ˆ„์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ๋ก : ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘ ์‹œ ์•ฝ 70์„ธ ๋ฐ 80์„ธ ์—ฐ๋ น์„ ๊ธฐ์ค€์œผ๋กœ ๊ธฐ๋Šฅ์  ์˜์กด์„ฑ๊ณผ ๋‡Œ์กธ์ค‘ ํ›„ ์‚ฌ๋ง ์œ„ํ—˜์ด ์œ ์˜๋ฏธํ•˜๊ฒŒ ์ฆ๊ฐ€ํ–ˆ๋‹ค. ๊ธฐ๋Šฅ์  ์˜์กด์„ฑ์˜ ๋ฐœ๋ณ‘ ์—ฐ๋ น์— ๋”ฐ๋ฅธ ์ฐจ์ด๋Š” ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘ ํ›„ ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ–ˆ์ง€๋งŒ, ๋‡Œ์กธ์ค‘ ๋ฐœ๋ณ‘ ์—ฐ๋ น๊ตฐ์— ๋”ฐ๋ฅธ ์‚ฌ๋ง ์œ„ํ—˜์˜ ์—ฐ๋ น๊ตฐ๊ฐ„ ์ฐจ์ด๋Š” ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ์œ ์˜๋ฏธํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜์ง€ ์•Š์•˜๋‹ค.open์„

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    Classification of Phonocardiogram Recordings Using Vision Transformer Architecture

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์ง€๋Šฅ์ •๋ณด์œตํ•ฉํ•™๊ณผ, 2023. 2. ์„œ๋ด‰์›.์‹ฌ์žฅ ์งˆํ™˜์˜ ์ง„๋‹จ์€ ์ค‘์š”ํ•œ ์˜ํ•™์  ์ •๋ณด์ธ ์‹ฌ์žฅ์˜ ์ƒํƒœ ๋ฐ ๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์‹ฌ์Œ(heart sound)์„ ์ธก์ •ํ•˜๋Š” ๊ฒƒ์œผ๋กœ๋ถ€ํ„ฐ ์‹œ์ž‘๋œ๋‹ค. ์ฒญ์ง„๊ธฐ๋ฅผ ํ†ตํ•ด ์‹ฌ์Œ์„ ๋“ฃ๊ณ  ์ง„๋‹จํ•˜๋Š” ๊ฒƒ์€ ์ •๋ฐ€๊ฒ€์‚ฌ์— ๋น„ํ•ด ์ •ํ™•๋„๊ฐ€ ๋–จ์–ด์ง€์ง€๋งŒ, ์šฉ์ดํ•˜๊ณ  ๋น„์šฉ์ด ๊ฑฐ์˜ ๋“ค์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ์ง„๋‹จ์— ์žˆ์–ด ํ•„์ˆ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ๊ฑด๊ฐ•ํ•œ ์‚ฌ๋žŒ์—๊ฒŒ์„œ๋Š” ๊ทœ์น™์ ์ด๊ณ  ๋ช…ํ™•ํ•œ ์‹ฌ์žฅ ๋ฐ•๋™ ์†Œ๋ฆฌ๊ฐ€ ๋“ค๋ฆฌ์ง€๋งŒ, ๊ทธ๋ ‡์ง€ ์•Š์€ ์‚ฌ๋žŒ์—๊ฒŒ์„œ๋Š” ์‹ฌ์žฅ ์†Œ๋ฆฌ์™€ ํ•จ๊ป˜ ์žก์Œ์ด ํ•จ๊ป˜ ๋“ค๋ฆฌ๊ธฐ๋„ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์žก์Œ์„ ์‹ฌ์žก์Œ(heart murmur) ์ด๋ผ๊ณ  ํ•˜๋ฉฐ, ์‹ฌ์žก์Œ์˜ ํŠน์ง•๊ณผ ์žก์Œ์ด ๋“ค๋ฆฌ๋Š” ์œ„์น˜ ๋“ฑ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‹ฌ์žฅ๋ณ‘์„ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹ฌ์Œ ๋ฐ์ดํ„ฐ๋ฅผ ๋…น์Œํ•ด์„œ ๋งŒ๋“ค์–ด์ง„ ์‹ฌ์Œ๋„(PCG; Phonocardiograms) ๋ฐ์ดํ„ฐ๋กœ ์ด ์‚ฌ๋žŒ์ด ์‹ฌ์žฅ๋ณ‘ ํ™˜์ž์ธ์ง€ ์œ ๋ฌด๋ฅผ ํƒ์ง€ํ•˜๊ฑฐ๋‚˜, ์‹ฌ์žฅ ์†Œ๋ฆฌ์— ์ด์ƒ์ด ์žˆ๋Š”์ง€ ๋“ฑ์„ ํƒ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹ฌ์Œ์—์„œ ๋น„์ •์ƒ์ ์ธ ์‹ฌ์žฅ ๊ธฐ๋Šฅ์„ ๊ฐ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ์ž๋™ํ™”๋œ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์€ ๊ฐœ๋ฐœ๋„์ƒ๊ตญ๊ณผ ๊ฐ™์ด ์ „๋ฌธ๊ฐ€์™€ ์ž๋ณธ์ด ๋ถ€์กฑํ•œ ๋‚˜๋ผ์˜ ์‹ฌ์žฅ๋ณ‘์œผ๋กœ ๊ณ ํ†ต๋ฐ›๋Š” ์‚ฌ๋žŒ๋“ค์—๊ฒŒ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ ์ฃผ์ œ์ด๋‹ค. ์˜๋ฃŒ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ๋ธ๋ง ํ•˜๋Š” ๋ฐฉ๋ฒ•์—๋Š” ๊ณ ์ „์ ์ธ ๊ธฐ๊ณ„ํ•™์Šต(Machine Learning) ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ, ๋”ฅ๋Ÿฌ๋‹(Deep Learning) ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ์˜๋ฃŒ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋ง์€ ํŠน์„ฑ(feature)์„ ์ง์ ‘ ์ถ”์ถœํ•ด์•ผ ํ•˜๋ฏ€๋กœ, ์‚ฌ์šฉํ•˜๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ์—ฐ๊ตฌ์ž์˜ ์‚ฌ์ „์ง€์‹๊ณผ ์ „์ฒ˜๋ฆฌ(pre-processing) ๋ฐฉ๋ฒ•๋“ค์ด ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋ฐ˜๋ฉด ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ์€ ์ด๋Ÿฌํ•œ ํŠน์„ฑ๊นŒ์ง€๋„ ๋ชจ๋ธ์ด ์ง์ ‘ ํ•™์Šตํ•˜์—ฌ ์ถ”์ถœํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์‚ฌ์šฉํ•˜๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์ž์˜ ์‚ฌ์ „์ง€์‹๊ณผ ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ•์˜ ์˜ํ–ฅ์ด ๋น„๊ต์  ๋‚ฎ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์˜๋ฃŒ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋ง์—๋Š” ๊ธฐ๊ณ„ํ•™์Šต ๋ชจ๋ธ๋“ค์ด ๋งŽ์ด ์‚ฌ์šฉ๋˜์—ˆ์œผ๋‚˜, ์ตœ๊ทผ ์˜๋ฃŒ๋ฐ์ดํ„ฐ ๋ถ„์•ผ์—์„œ๋Š” ๋”ฅ๋Ÿฌ๋‹ ๋ถ„์•ผ์—์„œ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•ด์„œ ๊ธฐ์กด ์˜๋ฃŒ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋ง์—์„œ ์„ฑ๋Šฅ์„ ๋†’์ด๋Š” ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ณ ์ „์ ์ธ ๊ธฐ๊ณ„ํ•™์Šต๋“ค๋ณด๋‹ค ๋” ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. ํ•˜์ง€๋งŒ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ๋“ค์€ ๋Œ€๋ถ€๋ถ„ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ํ•ด์„์„ ํ•  ์ˆ˜ ์—†์–ด, ์ „๋ฌธ๊ฐ€์˜ ์ง„๋‹จ์— ๋„์›€์„ ์ฃผ๋Š” ๊ฒƒ์— ์–ด๋ ค์›€์„ ๊ฒช๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์‚ฌ์šฉ๋˜๋Š” Vision Transformer ๊ตฌ์กฐ์˜ ๊ฒฝ์šฐ์—๋Š” ์…€ํ”„ ์–ดํ…์…˜(self-attention)์ด ํฌํ•จ๋˜์–ด ์žˆ์œผ๋ฉฐ, ์ด๋Š” ๊ฒฐ๊ณผ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ์–ดํ…์…˜ ์ ์ˆ˜(attention score)๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ์ด๋ฅผ ์–ดํ…์…˜ ๋งˆ์Šคํฌ(attention mask)๋กœ ์‹œ๊ฐํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ๊ณผ, ์ปดํ“จํ„ฐ ๋น„์ „(Computer Vision)๋ถ„์•ผ์˜ ์ด๋ฏธ์ง€ ๋ถ„๋ฅ˜(Image Classification) ํƒœ์Šคํฌ(task)์—์„œ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ์žฅ์ ์„ ๊ฐ€์ง„ ๋ชจ๋ธ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์—ฌ๋Ÿฌ ์ฒญ์ง„ ์œ„์น˜์—์„œ ์ธก์ •๋œ ์‹ฌ์žฅ ์†Œ๋ฆฌ ๋…น์Œ์—์„œ ์žก์Œ์˜ ์œ ๋ฌด์™€ ์ž„์ƒ ๊ฒฐ๊ณผ๋ฅผ ์ž˜ ๊ฐ์ง€ํ•˜๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณด๋‹ค ๋†’์€ ๋ถ„๋ฅ˜ ์„ฑ๋Šฅ๊ณผ ๊ฒฐ๊ณผ์˜ ํ•ด์„์— ๋„์›€์„ ์ฃผ๊ธฐ ์œ„ํ•ด ์‹œ๊ฐ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ–ˆ๋‹ค. ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ ์‹ ํ˜ธ์˜ ๋ฆฌ์ƒ˜ํ”Œ๋ง(resampling)์ด๋‚˜ ํ•„ํ„ฐ๋ง(filtering) ์—†์ด ์‹ฌ์žฅ ์†Œ๋ฆฌ ์‹ ํ˜ธ๋ฅผ ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ(spectrogram)์œผ๋กœ ๋ณ€ํ™˜ํ•˜๊ณ , ๋ณ€ํ™˜๋œ ์ด๋ฏธ์ง€๋ฅผ ํ™˜์ž์˜ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ์ •๋ณด์™€ ํ•จ๊ป˜ ์ž…๋ ฅ ๋ฐ›์•„ ์‹ฌ์žก์Œ๊ณผ ์ž„์ƒ ๊ฒฐ๊ณผ๋ฅผ ์ถ”๋ก (inference)ํ•œ๋‹ค. ์ž„์ƒ ๊ฒฐ๊ณผ ์‹๋ณ„ ์ž‘์—…์˜ ๊ฒฝ์šฐ ํ…Œ์ŠคํŠธ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ๋Œ€ํšŒ ๋น„์šฉ ํ•จ์ˆ˜ ์ ์ˆ˜ 11943์„, ์‹ฌ์žก์Œ ๋ถ„๋ฅ˜์— ๋Œ€ํ•ด์„œ๋Š” 0.69์˜ ๊ฐ€์ค‘์น˜ ์ •ํ™•๋„๋ฅผ ๊ธฐ๋กํ–ˆ๋‹ค. ๋˜ํ•œ ๋ชจ๋ธ์—๋Š” ์–ด๋Š ๋ถ€๋ถ„์„ ๋ณด๊ณ  ํŒ๋‹จํ–ˆ๋Š”์ง€ ์‹œ๊ฐํ™”๊ฐ€ ๊ฐ€๋Šฅํ•œ ์–ดํ…์…˜ ๋งˆ์Šคํฌ๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ๋‹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.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ 4 ์ œ 2 ์žฅ ์„ ํ–‰ ์—ฐ๊ตฌ 7 ์ œ 1 ์ ˆ ์‹ฌ์žฅ ์†Œ๋ฆฌ ๋ถ„๋ฅ˜ 7 ์ œ 2 ์ ˆ ์†Œ๋ฆฌ์˜ ์‹œ๊ฐํ™” 12 ์ œ 3 ์ ˆ Vision Transformer 16 ์ œ 4 ์ ˆ ๋ฐ์ดํ„ฐ ์ฆ๊ฐ• 22 ์ œ 3 ์žฅ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐ ์„ค๊ณ„ 28 ์ œ 1 ์ ˆ ๋ฐ์ดํ„ฐ์…‹ 28 ์ œ 2 ์ ˆ ์ „์ฒ˜๋ฆฌ ๋ฐ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ์…‹ ์ƒ์„ฑ 32 ์ œ 3 ์ ˆ ๋ชจ๋ธ ํ•™์Šต 37 ์ œ 4 ์žฅ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 41 ์ œ 1 ์ ˆ ํ‰๊ฐ€ ์ง€ํ‘œ 41 ์ œ 2 ์ ˆ ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์ ˆ์ œ ์—ฐ๊ตฌ 43 ์ œ 3 ์ ˆ ๋Œ€ํšŒ ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ 47 ์ œ 5 ์žฅ ๋…ผ์˜ 50 ์ œ 1 ์ ˆ ๋Œ€ํšŒ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 50 ์ œ 2 ์ ˆ ์–ดํ…์…˜ ๋งˆ์Šคํฌ 52 ์ œ 3 ์ ˆ ํ–ฅํ›„ ๊ณ„ํš 56 ์ œ 4 ์ ˆ ํ•œ๊ณ„ 58 ์ œ 6 ์žฅ ๊ฒฐ๋ก  59 ์ฐธ๊ณ ๋ฌธํ—Œ 61 Abstract 68์„
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