4 research outputs found

    Dual-Frequency SSVEP-based BCI for Reducing Eye Fatigue and Improving Classification Rate

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2016. 2. ๋ฐ•๊ด‘์„.The steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its high signal-to-noise ratio (SNR), and little requirement for training. However, the stimulus for evoking SSVEP causes high visual fatigue and has a risk of epileptic seizure. Furthermore, stimulation frequency is limited and the SSVEP amplitude diminishes when a monitor is used as a stimulator. In this thesis, a dual-frequency SSVEP is examined to resolve the aforementioned issues. Employing dual-frequency SSVEPs, two novel SSVEP-based BCIs are introduced to decrease eye fatigue and use harmonic frequencies with increased performance. First, the spectral characteristics of dual-frequency SSVEPs are investigated and frequency recognition methods for dual-frequency SSVEPs are suggested. Three methods based on power spectral density analysis (PSDA) and two methods based on canonical correlation analysis (CCA) were tested. The proposed CCA with a novel reference signal exhibited the best BCI performance, and the use of harmonic components improved the classification rate of the dual-frequency SSVEP. Second, the dual-frequency SSVEP response to an amplitude-modulated stimulus (AM-SSVEP) was explored to verify its performance with reduced eye fatigue. An amplitude-modulated stimulus was generated using the product of two sine waves at a carrier frequency (fc) and a modulating frequency (fm). The carrier frequency was higher than 40 Hz to reduce eye fatigue, and the modulating frequency ranged around the ฮฑ-band (9โ€“12 Hz) to utilize low-frequency harmonic information. The feasibility of AM-SSVEP with high BCI performance and low eye fatigue was confirmed through offline and online experiments. Using an optimized combination of the harmonic frequencies, the online experiments demonstrated that the accuracy of the AM-SSVEP was 97%, equivalent to that of the low-frequency SSVEP. Furthermore, subject evaluation indicated that an AM stimulus caused lower eye fatigue and less perception of flickering than a low-frequency stimulus, in a manner similar to a high-frequency stimulus. Third, a novel dual-frequency SSVEP-based hybrid SSVEP-P300 speller is introduced to overcome the frequency limitations and improve the performance. The hybrid speller consists of nine panels flickering at different frequencies. Each panel contains four different characters that appear in a random sequence. The flickering panel and the periodically updating character evoke the dual-frequency SSVEP, and the oddball stimulus of the target character evokes the P300. Ten subjects participated in offline and online experiments, in which accuracy and information transfer rate (ITR) were compared with those of conventional SSVEP and P300 spellers. The offline analysis revealed that the proposed speller elicited dual-frequency SSVEP. Moreover, the dual-frequency SSVEP significantly improved the SSVEP classification rate and ITR with a monitor in online experiments by 4 % accuracy and 18.8 bpm ITR. In conclusion, the proposed dual-frequency SSVEP-based BCIs reduce eye fatigue and improve SSVEP classification rate. The results indicate that this study provides a promising approach to make SSVEP-based BCIs more reliable and efficient for practical use.1. Introduction 1 1.1. Brain-Computer Interface 1 1.1.1. Basic Concepts 1 1.1.2. SSVEP-based BCIs 2 1.1.3. P300-based BCIs 5 1.1.4. Hybrid SSVEP-P300 BCIs 6 1.2. Motivation and Objectives 7 2. Frequency Recognition Methods for DFSSVEP-based BCI 11 2.1. Basic Concepts 11 2.2. DFSSVEP Recognition Methods 16 2.2.1. PSDA-based Methods 17 2.2.2. CCA-based Methods 20 2.3. Offline Analysis 23 2.3.1. Dual-Frequency Stimulus 23 2.3.2. Experimental Settings 24 2.3.3. Spectral Analysis of DFSSVEP 25 2.3.4. Signal Processing 26 2.4. Results 27 2.4.1. Harmonic Frequency 27 2.4.2. Comparison of Recognition Rates 28 2.5. Conclusion 31 3. DFSSVEP-based BCI for Reducing Eye Fatigue 33 3.1. Basic Concepts 33 3.1.1. Amplitude Modulation Technique 33 3.1.2. Amplitude-Modulated Stimuli for Evoking AM-SSVEP 35 3.2. Methods 38 3.2.1. Subjects and Experimental Settings 38 3.2.2. Offline Experiments 41 3.2.3. EEG Analysis 43 3.2.4. Online Experiments 45 3.3. Results 50 3.3.1. Harmonics of AM-SSVEP 50 3.3.2. Offline Analysis 54 3.3.3. CFC for Online Analysis 57 3.3.4. Online Analysis 59 3.3.5. Subject Evaluation 64 3.4. Discussion 66 3.4.1. Combining of Low- and High-Frequency SSVEPs 66 3.4.2. AM Harmonic Frequencies in CFC 70 3.4.3. Error Analysis 71 3.4.4. Effects of Environmental Illumination 74 3.5. Conclusion 76 4. DFSSVEP-based Hybrid BCI for Improving Classification Rate 79 4.1. Basic Concepts 79 4.2. Methods 85 4.2.1. Experimental Setting 85 4.2.2. Experimental Procedure 88 4.2.3. Signal Processing 89 4.2.4. Statistical Comparison of the EEG Responses 91 4.2.5. BCI Performance 92 4.3. Results 94 4.3.1. EEG Response to the Hybrid Speller 94 4.3.2. Offline Analysis 99 4.3.3. Online Analysis 102 4.4. Discussion 104 4.4.1. DFSSVEP 104 4.4.2. ITR Comparison with Conventional Spellers 109 4.4.3. ITR Comparison with Previous Studies 110 4.4.4. ITR with Different Visual Angle 114 4.4.5. Limitations 117 4.5. Conclusion 118 5. Conclusion 119 6. References 123 ๊ตญ๋ฌธ ์ดˆ๋ก 133Docto

    A Study on Utilization of Maternity Protection Policy: Focused on the Relational Analysis to Performance

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› : ์ •์ฑ…ํ•™๊ณผ, 2014. 2. ์ด์ˆ˜์˜.์—ฌ์„ฑ์˜ ๊ฒฝ์ œํ™œ๋™์—๋Š” ๋‹ค์–‘ํ•œ ์žฅ์• ๋ฌผ์ด ์กด์žฌํ•œ๋‹ค. ํŠนํžˆ ์ถœ์‚ฐ๊ณผ ์œก์•„๋กœ ์ธํ•˜์—ฌ ๊ฒฝ๋ ฅ ๋‹จ์ ˆ์ด ๋ฐœ์ƒํ•˜๊ธฐ ์‰ฌ์›Œ ์กฐ์ง ๋ชฐ์ž…์— ์–ด๋ ค์›€์ด ์กด์žฌํ•˜๊ณ  ์ด๋กœ ์ธํ•ด ์—ฌ์„ฑ์ธ๋ ฅ์˜ ์ผ๊ณผ ๊ฐ€์ • ์–‘๋ฆฝ์€ ์‰ฝ์ง€ ์•Š์€ ์ƒํ™ฉ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ  ๋•Œ๋ฌธ์— ์‚ฌํšŒํ•™์ž Skocpol์€ ์—ฌ์„ฑ์˜ ์ถœ์‚ฐ ๋ฐ ์œก์•„๋Š” ๋‚จ์„ฑ์˜ ๊ตฐ๋ณต๋ฌด์™€ ์œ ์‚ฌํ•˜๊ฒŒ ์‚ฌํšŒ ์ •์ฑ…์ ์œผ๋กœ ๊ณต๊ณต์žฌ(public good)์˜ ์„ฑ๊ฒฉ์ด ๊ฐ•ํ•˜์—ฌ ์ •์ฑ…์ ์ธ ๊ด€์‹ฌ์˜ ๋Œ€์ƒ์ด ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ํ•˜์˜€๋‹ค(Skocpol, 1992). ๊ทธ๋ฆฌ๊ณ  ์ •๋ถ€ ์ฐจ์›์—์„œ๋„ ์—ฌ์„ฑ์ธ๋ ฅ์˜ ํšจ๊ณผ์ ์ธ ํ™œ์šฉ์„ ์œ„ํ•˜์—ฌ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ์ ๊ทน์ ์ธ ํ™œ์šฉ์„ ๋„๋ชจํ•˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ๋ณธ ์—ฐ๊ตฌ๋Š” ์—ฌ์„ฑ์˜ ์ผ-๊ฐ€์ • ์–‘๋ฆฝ์„ ์œ„ํ•ด ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ•„์š”์„ฑ์ด ์ฆ๋Œ€๋˜๊ณ  ์žˆ๋Š” ํ˜„ ์ƒํ™ฉ๊ณผ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์—์„œ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋Š” ์ œ๋„์˜ ์‹ค์ œ๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ œ๋„์˜ ํ™œ์šฉ๋„๊ฐ€ ๋–จ์–ด์ง€๋Š” ๋””์ปคํ”Œ๋ง ํ˜„์ƒ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ๋„์ž…์ด ์•„๋‹Œ ํ™œ์šฉ์— ์ดˆ์ ์„ ๋งž์ถฐ ์กฐ์ง์„ฑ๊ณผ์™€์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ „๋žต์  ์ธ์ ์ž์›๊ด€๋ฆฌ๊ณผ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ด๋ก ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„๋ฅผ ์กฐ์ง์˜ ์ „๋žต๊ณผ ๊ฐœ๋ณ„ ์ธ์‚ฌ์ œ๋„๋“ค๊ณผ ์กฐํ™”๋ฅผ ์ด๋ฃจ์–ด์•ผํ•˜๋Š” ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„์˜ ํ•˜๋‚˜๋กœ ์ดํ•ดํ•˜์—ฌ, ์กฐ์ง์˜ ์ „๋žต์œ ํ˜• ๋ฐ ๊ฐœ๋ณ„ ์ธ์‚ฌ๊ด€๋ฆฌ์ œ๋„์™€ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„๊ฐ€ ์ƒํ˜ธ์ž‘์šฉํ•˜์—ฌ ์กฐ์ง์„ฑ๊ณผ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์‹ค์ฆ๋ถ„์„ ํ•˜์˜€๋‹ค. ๋จผ์ € ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ์ •๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ์กฐ์ง์„ฑ๊ณผ์ธ ์žฌ๋ฌด์„ฑ๊ณผ๋Š” ์ฆ๊ฐ€ํ•˜๊ณ , HRM์„ฑ๊ณผ์ธ ์ด์ง๋ฅ ์€ ํ•˜๋ฝํ•œ๋‹ค๊ณ  ๊ฐ€์„ค์„ ์„ค์ •ํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ๋Š” ํ˜์‹ ์ „๋žต์˜ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์™€ ์กฐ์ง์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์กฐ์ ˆํšจ๊ณผ์— ๊ด€ํ•œ ๊ฐ€์„ค์„ ์„ค์ •ํ•˜์—ฌ, ํ˜์‹ ์ „๋žต์„ ์ทจํ•œ ์กฐ์ง์˜ ๊ฒฝ์šฐ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์™€ ์กฐ์ง์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์‚ดํŽด๋ณด์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋งˆ์ง€๋ง‰์œผ๋กœ๋Š” ์ธ์‚ฌ๊ด€๋ฆฌ ํ”„๋กœ์„ธ์Šค์˜ ๊ฐ ๋‹จ๊ณ„๋ณ„ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„๋“ค์ด ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์™€ ์ด์ง๋ฅ  ๊ฐ„์˜ ๋ฏธ์น˜๋Š” ์กฐ์ ˆํšจ๊ณผ์— ๋Œ€ํ•œ ๊ฐ€์„ค์„ ์„ค์ •ํ•˜์˜€๋‹ค. ์œ„์™€ ๊ฐ™์€ ๊ฐ€์„ค์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ•œ๊ตญ๋…ธ๋™์—ฐ๊ตฌ์›์˜ ์‚ฌ์—…์ฒดํŒจ๋„์กฐ์‚ฌ ์ค‘ 2009๋…„ ์„ค๋ฌธ๊ฒฐ๊ณผ๋ฅผ ํ™œ์šฉํ•ด ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์‹ค์ฆ๋ถ„์„ ๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด, ๋จผ์ € ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„๋ฅผ ์ ๊ทน์ ์œผ๋กœ ํ™œ์šฉํ• ์ˆ˜๋ก ์กฐ์ง์˜ ์žฌ๋ฌด์„ฑ๊ณผ์ธ ๋งค์ถœ์•ก์€ ์ฆ๊ฐ€ํ•˜๊ณ , HRM์„ฑ๊ณผ์ธ ์ด์ง๋ฅ ์„ ํ•˜๋ฝํ•œ๋‹ค๋Š” ๊ฒฐ๋ก ์ด ๋„์ถœ๋˜์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์กฐ์ง์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์—์„œ์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋ถ„์„๊ฒฐ๊ณผ, ๋จผ์ € ํ˜์‹ ์ „๋žต์€ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์™€ ์กฐ์ง์„ฑ๊ณผ์— ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๊ฐœ๋ณ„ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„๋“ค๋„ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์ด์ง๋ฅ  ๊ฐ„์˜ ๋ถ€์˜ ๊ด€๊ณ„๋ฅผ ๊ฐ•ํ™”์‹œํ‚ค์ง€๋Š” ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค๋งŒ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„ ์ค‘ ์—ฐ๋ด‰์ œ์˜ ๊ฒฝ์šฐ๋Š” ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์™€ ์ด์ง๋ฅ  ๊ฐ„์˜ ๋ถ€์˜ ๊ด€๊ณ„๋ฅผ ์•ฝํ™”์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ์ด ์กฐ์ง์„ฑ๊ณผ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ๋‹ค๋Š” ์ ์„ ๋ฐํžˆ๋ฉด์„œ ๊ธฐ์—…์˜ ์กฐ์ง์„ฑ๊ณผ ํ–ฅ์ƒ์„ ์œ„ํ•ด ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ์ ๊ทน์ ์ธ ํ™œ์šฉ์„ ๋„๋ชจํ•˜๊ณ , ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์™€ ์กฐ์ง์ „๋žต ๋ฐ ๋‹ค์–‘ํ•œ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„๋“ค ๊ฐ„์˜ ๊ด€๊ณ„ ์—ฐ๊ตฌ์˜ ์ถœ๋ฐœ์ ์ด ๋˜๊ณ ์ž ํ•œ๋‹ค.์ œ1์žฅ ์„œ๋ก  -------------------------------------------- 1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ------------------------------- 1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ---------------------------------------- 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  ---------------------------------------- 4 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ --------------------------------------- 5 ์ œ2์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ----------------------7 ์ œ1์ ˆ ์ด๋ก ์  ๋ฐฐ๊ฒฝ --------------------------------------- 7 1. ์ „๋žต์  ์ธ์ ์ž์›๊ด€๋ฆฌ์™€ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„ --------- 7 2. ์ธ์‚ฌ๊ด€๋ฆฌ์™€ ๊ฒฝ์˜์„ฑ๊ณผ๊ฐ„์˜ ๊ด€๊ณ„ : ์ž์›๊ธฐ๋ฐ˜๊ด€์  --------- 12 3. ๊ฒฝ์Ÿ์ „๋žต ------------------------------------------ 14 4. ์ธ์ ์ž์›์˜ ๋‹ค์–‘์„ฑ ๊ด€๋ฆฌ์™€ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„ --------------- 16 5. ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„ ---------------------------------------18 ์ œ2์ ˆ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  -----------------------29 ์ œ3์žฅ ๊ฐ€์„ค ์„ค์ • ๋ฐ ์—ฐ๊ตฌ ์„ค๊ณ„ -----------------------32 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฐ€์„ค ์„ค์ • ------------------------------------32 1. ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์กฐ์ง์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ๊ฐ€์„ค --32 2. ์ „๋žต์œ ํ˜•์— ๋”ฐ๋ฅธ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์กฐ์ง์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ๊ฐ€์„ค ---------------------------------------------34 3. ๊ณ ๋ชฐ์ž… ์ธ์‚ฌ๊ด€๋ฆฌ์— ๋”ฐ๋ฅธ ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ HRM์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ๊ฐ€์„ค --------------------------------------- 36 ์ œ2์ ˆ ์—ฐ๊ตฌ ์„ค๊ณ„ 1. ๋ณ€์ˆ˜์˜ ์„ ์ • ----------------------------------------39 2. ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜ --------------------------------- 43 ์ œ3์ ˆ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ ๋ฐ ๋ถ„์„ ๋ฐฉ๋ฒ• ------------------------ 45 1. ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ ------------------------------------- 45 2, ๋ถ„์„๋ฐฉ๋ฒ• ------------------------------------------ 45 ์ œ4์žฅ ์‹ค์ฆ๋ถ„์„ ๊ฒฐ๊ณผ ---------------------------------- 48 ์ œ1์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„ ------------------------------------ 48 ์ œ2์ ˆ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ ------------------------------------ 49 ์ œ3์ ˆ ์‹ค์ฆ๋ถ„์„ ---------------------------------------- 51 1. ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์กฐ์ง์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„ ๋ถ„์„ -------- 51 2. ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์ด์ง๋ฅ  ๊ฐ„์˜ ๊ด€๊ณ„์—์„œ ํ˜์‹ ์ „๋žต์˜ ์กฐ์ ˆํšจ๊ณผ ๋ถ„์„ --------------------------------------------- 56 3. ๋ชจ์„ฑ๋ณดํ˜ธ์ œ๋„์˜ ํ™œ์šฉ๊ณผ ์ด์ง๋ฅ  ๊ฐ„์˜ ๊ด€๊ณ„์—์„œ ๊ณ ๋ชฐ์ž… ์ธ์ ์ž์›๊ด€๋ฆฌ์ œ๋„์˜ ์กฐ์ ˆํšจ๊ณผ ๋ถ„์„ ------------------------------- 62 4. ๊ฐ€์„ค๊ฒ€์ฆ ๊ฒฐ๊ณผ ์š”์•ฝ ๋ฐ ํ•ด์„ -------------------------79 ์ œ5์žฅ ๊ฒฐ๋ก  ------------------------------------------- 87 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ ๋ฐ ์ •์ฑ…์  ํ•จ์˜ --------------------87 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๊ณผ์ œ ----------------------90 ์ฐธ๊ณ ๋ฌธํ—Œ ----------------------------------------------91 Abstract ----------------------------------------------96Maste

    ํ˜•๊ด‘์ œ์ž๋ฆฌ๋ณดํ•ฉ๋ฒ• ์ƒ 17๋ฒˆ ์—ผ์ƒ‰์ฒด ๋™์›์ฒด์˜ ์ˆ˜์  ์ฆ๊ฐ€๋ฅผ ๋ณด์ด๋Š” ์นจ์œค์„ฑ ์œ ๋ฐฉ์•”์—์„œ HER2์˜ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2015. 2. ๋ฐ•์†Œ์—ฐ.Background: A subset of breast cancers shows increased copy numbers of chromosome 17 centromere on in situ hybridization (ISH). However, recent studies have revealed that true polysomy 17 is a rare event in breast cancer, and that an increased copy number of centromere 17 represents amplification or copy number gain in and around the centromeric region. In such instances, the use of chromosome enumeration probe targeting centromere 17 (CEP17) in HER2 ISH is limitedthus, alternative methods for precise assessment of HER2 status are necessary. Performing ISH using probes for other genes on chromosome 17 as additional reference genes has been proposed by 2013 ASCO/CAP guidelines as well as several previous studies. In this study, we applied this method to breast cancers with increased CEP17 copy numbers (โ‰ฅ 2.6) and compared it with conventional methods based on the 2007 and 2013 ASCO/CAP guidelines. Methods: After reviewing all HER2 fluorescence in situ hybridization (FISH) reports recorded from June 2004 to December 2011 at Seoul National University Bundang Hospital, we identified 300 cases (29.6%) with CEP17 copy number โ‰ฅ 2.6 from 1013 breast cancers. We performed FISH with probes for RARA, SMS, and TP53 genes on 253 breast cancers that had available tissue blocks, using tissue microarrays. If one or more gene had a mean copy number <2.6 the largest number for that gene(s) was chosen as an alternative to the CEP17 copy number, and we re-graded the HER2 status based on HER2: alternative gene ratio. After re-grading the HER2 status, we selected 8 cases which represent the various patterns of copy number alterations on chromosome 17, and performed high-resolution array-based comparative genomic hybridization (aCGH) to confirm the genomic copy number variation. Results: Of the 243 cases in which re-grading were possible, only 2 had copy numbers โ‰ฅ2.6 for RARA, SMS and TP53 gene. Of 151 breast cancers classified as HER2 non-amplified by the 2007 ASCO/CAP guidelines using the HER2:CEP17 ratio (<1.8), 42 (27.8%) were re-graded as amplified and 33 (21.8%) as equivocal after FISH using additional reference genes. Of the 101 HER2 non-amplified cases by the 2013 ASCO/CAP guidelines, 2 (2.0%) were reclassified as amplified and 24 (23.8%) as equivocal. Of 46 equivocal cases, 35 (76.1%) were re-graded as amplified. After re-grading, amplified cases were significantly increased, and the concordance between HER2 FISH and immunohistochemistry decreased. Of the 8 cases analyzed by aCGH, six were upgraded from non-amplified to amplified by additional FISH studies. However, only 3 cases were proven to have HER2 amplification on aCGH. Two cases which were assumed to have true polysomy 17 by additional FISH studies were proven not to be polysomic. We also reviewed the pathologic features of the cases whose HER2 status were upgraded to be amplified by additional FISH, but some pathologic features were not matched with those of HER2-amplied tumors. Conclusion: Using additional reference genes in combination might be an option for accurate HER2 evaluation in breast cancer with increased CEP17 copy numbers. However, it has some limitations. It can cause over-grading of HER2 status, when the tumor has loss of new reference genes. Especially three genes that we used in current study (SMS, TP53 and RARA) were not suitable for alternative reference gene when used independently. Moreover, copy number alterations detected by additional FISH and those by aCGH were not well-correlated. Thus, use of alternative genes on chromosome 17 such as SMS, RARA and TP53 instead of CEP17 is not still suitable to be applied in daily practice. Additional studies to search the most stable gene that rarely shows copy number alteration will be needed.CONTENTS Abstract i Contents iv List of tables v List of figures vi List of abbreviations vii Introduction 1 Materials and Methods 5 Results 13 Discussion 40 Conclusion 48 Appendices 49 References 56 Abstract in Korean 65Docto

    ์ค‘์†Œ๊ธฐ์—…์˜ ๋‚˜์•„๊ฐˆ ๊ธธ :"์ฟ ์ฟ "์˜ ์„ฑ๊ณต

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    ์ฟ ์ฟ ๋Š” ์š”๋ฆฌ(cook)์™€ ๋ป๊พธ๊ธฐ์˜ ํ•ฉ์„ฑ์–ด๋กœ์„œ ๋ง›์žˆ๋Š” ์š”๋ฆฌ(์Œ์‹)๋ฅผ ๊ธฐ๋Œ€ํ•˜๋Š” ๋งˆ์Œ์˜ ํ‘œ์‹œ๋ฅผ ํ˜•์ƒํ™”ํ•˜์—ฌ ๊ฐœ๋ฐœํ•œ ๊ณ ์œ ์˜ ๋ธŒ๋žœ๋“œ์ž…๋‹ˆ๋‹ค. ๋ป๊พธ๊ธฐ(cuckoo)์˜ ์˜์–ดํ‘œ๊ธฐ์ธ ์ฟ ์ฟ  ๋Š” ๋ป๊พธ๊ธฐ ์‹œ๊ณ„๋ฅผ ์ƒ์ง•ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์‹œ๊ณ„๋งŒํผ ์ •ํ™•ํ•œ ์ œํ’ˆ์„ ๋งŒ๋“ค๊ฒ ๋‹ค๋Š” ์„ฑ๊ด‘์ „์ž์˜ ์‹ ๋…์„ ๋‹ด๊ณ  ์žˆ๋‹ค. ๊น๊น ๊ณผ ๊ผผ๊ผผ ์œผ๋กœ ํ†ตํ•˜๋Š” ์ฟ ์ฟ  ๋Š” 25๋…„๊ฐ„ ์••๋ ฅ๋ฐฅ์†ฅ์„ ๋งŒ๋“ค์–ด์˜จ ์„ฑ๊ด‘์ „์ž๊ฐ€ 1998๋…„ ์ถœ์‹œํ•œ ์ž์ฒด ๋ธŒ๋žœ๋“œ์ด๋‹ค. 1978๋…„ ์ฐฝ๋ฆฝ๋œ ์„ฑ๊ด‘์ „์ž๋Š” 1998๋…„ ์ž์ฒด ๋ธŒ๋žœ๋“œ ์ฟ ์ฟ  ๋ฅผ ๋งŒ๋“ค์–ด ์ผ๋ณธ๊ธฐ์—…๊ณผ ๊ตญ๋‚ด ๋Œ€๊ธฐ์—…์„ ์ œ์น˜๊ณ  ์‹œํŒ 1๋…„ ๋งŒ์— ์ „๊ธฐ์••๋ ฅ๋ฐฅ์†ฅ ์‹œ์žฅ ์ ์œ ์œจ 1์œ„๋ฅผ ์ฐจ์ง€ํ–ˆ๋‹ค. ์ฟ ์ฟ ๋Š” 1998๋…„ 250์–ต, 1999๋…„ 450์–ต, 2000๋…„ 800์–ต์›, 2001๋…„์—๋Š” 1,300์–ต์›์˜ ๋งค์ถœ์„ ๊ธฐ๋กํ•˜๋ฉด์„œ ์ „์ฒด์ „๊ธฐ๋ฐฅ์†ฅ ์‹œ์žฅ ์ ์œ ์œจ 42.3%๋ฅผ ์ฐจ์ง€ํ•˜๋Š” ๊ธฐ์—ผ์„ ํ† ํ•œ๋‹ค. ํ˜„์žฌ LG๋‚˜ ์‚ผ์„ฑ์ด ์ง€์†์ ์ธ ๊ฐ€๊ฒฉํ•˜๋ฝ ์ •์ฑ…์„ ํ†ตํ•ด ์ฟ ์ฟ ๋ฅผ ๊ณต๊ฒฉํ•˜๊ณ  ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋Œ€๊ธฐ์—…์˜ ์‹œ์žฅ ์ ์œ ์œจ์€ ๊ทธ๋‹ค์ง€ ๋ณ€ํ™”๊ฐ€ ์—†๋‹ค. ์‚ผ์„ฑ์˜ ์ „๊ธฐ๋ฐฅ์†ฅ์‹œ์žฅ ์ ์œ ์œจ์€ 2000๋…„ 21%, 2001๋…„ 19%, 2002๋…„ 14%๋กœ ์ ์ฐจ ํ•˜๋ฝํ•˜๊ณ  ์žˆ๊ณ  LG๋Š” ๊ฐ๊ฐ 12%, 17%, 18%๋กœ ์•ฝ๊ฐ„ ์ƒ์Šน์„ธ๋ฅผ ๋ณด์ธ๋‹ค. ์ด์— ๋ฐ˜ํ•ด ์„ฑ๊ด‘์ „์ž์˜ ์‹œ์žฅ์ ์œ ์œจ์€ 2000๋…„ 36%, 2001๋…„ 43%, 2002๋…„ 50%์— ๋‹ฌํ•˜๊ณ  ์žˆ๋‹ค ์ด๊ฒƒ์€ ์‹œ์žฅ์„ ์›€์ง์ด๋Š” ์›๋™๋ ฅ์ด ๊ฐ€๊ฒฉํ•˜๋‚˜์ผ ๊ฒƒ์ด๋ผ๋Š” ๋‹จ์ˆœ๋…ผ๋ฆฌ์— ์ •๋ฉด์œผ๋กœ ๋ฐ˜ํ•˜๋Š” ๊ฒฐ๊ณผ์ด๋‹ค ๊ฐ€์น˜ ์žˆ๋Š” ์ œํ’ˆ๏ผŒ ๊ฐ€์น˜ ์žˆ๋Š” ๊ฐ€๊ฒฉ์„ ์›์น™์œผ๋กœ ํ•˜๋Š” ์ฟ ์ฟ ๋Š” ์ง„์ •ํ•œ ๊ฐ€๊ฒฉ๊ฒฝ์Ÿ๋ ฅ์€ ์†Œ๋น„์ž๊ฐ€ ์ธ์ •ํ•˜๋Š” ํ•ฉ๋ฆฌ์ ์ธ ๊ฐ€๊ฒฉ์—์„œ ๋‚˜์˜จ๋‹ค๊ณ  ๋‹น๋‹นํ•˜๊ฒŒ ๋งํ•œ๋‹ค
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