14 research outputs found

    μ§ˆμ˜μ‘λ‹΅ μ‹œμŠ€ν…œμ„ μœ„ν•œ ν…μŠ€νŠΈ λž­ν‚Ή 심측 신경망

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    ν•™μœ„λ…Όλ¬Έ (박사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 전기·정보곡학뢀, 2020. 8. 정ꡐ민.The question answering (QA) system has attracted huge interests due to its applicability in real-world applications. This dissertation proposes novel ranking algorithms for the QA system based on deep neural networks. We first tackle the long-text QA that requires the model to understand the excessively large sequence of text inputs. To solve this problem, we propose a hierarchical recurrent dual encoder that encodes texts from word-level to paragraph-level. We further propose a latent topic clustering method that utilizes semantic information in the target corpus, and thus it increases the performance of the QA system. Secondly, we investigate the short-text QA, where the information in text pairs are limited. To overcome the insufficiency, we combine a pretrained language model and an enhanced latent clustering method to the QA model. This novel architecture enables the model to utilizes additional information, resulting in achieving state-of-the-art performance for the standard answer-selection tasks (i.e., WikiQA, TREC-QA). Finally, we investigate detecting supporting sentences for complex QA system. As opposed to the previous studies, the model needs to understand the relationship between sentences to answer the question. Inspired by the hierarchical nature of the text, we propose a graph neural network-based model that iteratively propagates necessary information between text nodes and achieve the best performance among existing methods.λ³Έ ν•™μœ„ 논문은 λ”₯ λ‰΄λŸ΄ λ„€νŠΈμ›Œν¬ 기반 μ§ˆμ˜μ‘λ‹΅ μ‹œμŠ€ν…œμ— κ΄€ν•œ λͺ¨λΈμ„ μ œμ•ˆν•œλ‹€. λ¨Όμ € κΈ΄ λ¬Έμž₯에 λŒ€ν•œ μ§ˆμ˜μ‘λ‹΅μ„ ν•˜κΈ° μœ„ν•΄μ„œ 계측 ꡬ쑰의 μž¬κ·€μ‹ κ²½λ§ λͺ¨λΈμ„ μ œμ•ˆν•˜μ˜€λ‹€. 이λ₯Ό 톡해 λͺ¨λΈμ΄ 주어진 λ¬Έμž₯을 짧은 μ‹œν€€μŠ€ λ‹¨μœ„λ‘œ 효율적으둜 λ‹€λ£° 수 있게 ν•˜μ—¬ 큰 μ„±λŠ₯ ν–₯상을 μ–»μ—ˆλ‹€. λ˜ν•œ ν•™μŠ΅ κ³Όμ •μ—μ„œ 데이터 μ•ˆμ— λ‚΄ν¬λœ 토픽을 μžλ™ λΆ„λ₯˜ν•˜λŠ” λͺ¨λΈμ„ μ œμ•ˆν•˜κ³ , 이λ₯Ό κΈ°μ‘΄ μ§ˆμ˜μ‘λ‹΅ λͺ¨λΈμ— λ³‘ν•©ν•˜μ—¬ μΆ”κ°€ μ„±λŠ₯ κ°œμ„ μ„ μ΄λ£¨μ—ˆλ‹€. μ΄μ–΄μ§€λŠ” μ—°κ΅¬λ‘œ 짧은 λ¬Έμž₯에 λŒ€ν•œ μ§ˆμ˜μ‘λ‹΅ λͺ¨λΈμ„ μ œμ•ˆν•˜μ˜€λ‹€. λ¬Έμž₯의 길이가 μ§§μ•„μ§ˆμˆ˜λ‘ λ¬Έμž₯ μ•ˆμ—μ„œ 얻을 수 μžˆλŠ” μ •λ³΄μ˜ 양도 μ€„μ–΄λ“€κ²Œ λœλ‹€. μš°λ¦¬λŠ” μ΄λŸ¬ν•œ 문제λ₯Ό ν•΄κ²°ν•˜κΈ° μœ„ν•΄, 사전 ν•™μŠ΅λœ μ–Έμ–΄ λͺ¨λΈκ³Ό μƒˆλ‘œμš΄ ν† ν”½ ν΄λŸ¬μŠ€ν„°λ§ 기법을 μ μš©ν•˜μ˜€λ‹€. μ œμ•ˆν•œ λͺ¨λΈμ€ μ’…λž˜ 짧은 λ¬Έμž₯ μ§ˆμ˜μ‘λ‹΅ 연ꡬ 쀑 κ°€μž₯ 쒋은 μ„±λŠ₯을 νšλ“ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ μ—¬λŸ¬ λ¬Έμž₯ μ‚¬μ΄μ˜ 관계λ₯Ό μ΄μš©ν•˜μ—¬ 닡변을 μ°Ύμ•„μ•Ό ν•˜λŠ” μ§ˆμ˜μ‘λ‹΅ 연ꡬλ₯Ό μ§„ν–‰ν•˜μ˜€λ‹€. μš°λ¦¬λŠ” λ¬Έμ„œ λ‚΄ 각 λ¬Έμž₯을 κ·Έλž˜ν”„λ‘œ λ„μ‹ν™”ν•œ ν›„ 이λ₯Ό ν•™μŠ΅ν•  수 μžˆλŠ” κ·Έλž˜ν”„ λ‰΄λŸ΄ λ„€νŠΈμ›Œν¬λ₯Ό μ œμ•ˆν•˜μ˜€λ‹€. μ œμ•ˆν•œ λͺ¨λΈμ€ 각 λ¬Έμž₯의 관계성을 μ„±κ³΅μ μœΌλ‘œ κ³„μ‚°ν•˜μ˜€κ³ , 이λ₯Ό 톡해 λ³΅μž‘λ„κ°€ 높은 μ§ˆμ˜μ‘λ‹΅ μ‹œμŠ€ν…œμ—μ„œ 기쑴에 μ œμ•ˆλœ λͺ¨λΈλ“€κ³Ό λΉ„κ΅ν•˜μ—¬ κ°€μž₯ 쒋은 μ„±λŠ₯을 νšλ“ν•˜μ˜€λ‹€.1 Introduction 1 2 Background 8 2.1 Textual Data Representation 8 2.2 Encoding Sequential Information in Text 12 3 Question-Answer Pair Ranking for Long Text 16 3.1 Related Work 18 3.2 Method 19 3.2.1 Baseline Approach 19 3.2.2 Proposed Approaches (HRDE+LTC) 22 3.3 Experimental Setup and Dataset 26 3.3.1 Dataset 26 3.3.2 Consumer Product Question Answering Corpus 30 3.3.3 Implementation Details 32 3.4 Empirical Results 34 3.4.1 Comparison with other methods 35 3.4.2 Degradation Comparison for Longer Texts 37 3.4.3 Effects of the LTC Numbers 38 3.4.4 Comprehensive Analysis of LTC 38 3.5 Further Investigation on Ranking Lengthy Document 40 3.5.1 Problem and Dataset 41 3.5.2 Methods 45 3.5.3 Experimental Results 51 3.6 Conclusion 55 4 Answer-Selection for Short Sentence 56 4.1 Related Work 57 4.2 Method 59 4.2.1 Baseline approach 59 4.2.2 Proposed Approaches (Comp-Clip+LM+LC+TL) 62 4.3 Experimental Setup and Dataset 66 4.3.1 Dataset 66 4.3.2 Implementation Details 68 4.4 Empirical Results 69 4.4.1 Comparison with Other Methods 69 4.4.2 Impact of Latent Clustering 72 4.5 Conclusion 72 5 Supporting Sentence Detection for Question Answering 73 5.1 Related Work 75 5.2 Method 76 5.2.1 Baseline approaches 76 5.2.2 Proposed Approach (Propagate-Selector) 78 5.3 Experimental Setup and Dataset 82 5.3.1 Dataset 82 5.3.2 Implementation Details 83 5.4 Empirical Results 85 5.4.1 Comparisons with Other Methods 85 5.4.2 Hop Analysis 86 5.4.3 Impact of Various Graph Topologies 88 5.4.4 Impact of Node Representation 91 5.5 Discussion 92 5.6 Conclusion 93 6 Conclusion 94Docto

    μ†Œν˜•λ™λ¬Όμ˜ λ‡Œμ‹ κ²½ μžκ·Ήμ„ μœ„ν•œ μ™„μ „ μ΄μ‹ν˜• μ‹ κ²½μžκ·ΉκΈ°

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    ν•™μœ„λ…Όλ¬Έ(박사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :κ³΅κ³ΌλŒ€ν•™ 전기·정보곡학뢀,2020. 2. κΉ€μ„±μ€€.In this study, a fully implantable neural stimulator that is designed to stimulate the brain in the small animal is described. Electrical stimulation of the small animal is applicable to pre-clinical study, and behavior study for neuroscience research, etc. Especially, behavior study of the freely moving animal is useful to observe the modulation of sensory and motor functions by the stimulation. It involves conditioning animal's movement response through directional neural stimulation on the region of interest. The main technique that enables such applications is the development of an implantable neural stimulator. Implantable neural stimulator is used to modulate the behavior of the animal, while it ensures the free movement of the animals. Therefore, stable operation in vivo and device size are important issues in the design of implantable neural stimulators. Conventional neural stimulators for brain stimulation of small animal are comprised of electrodes implanted in the brain and a pulse generation circuit mounted on the back of the animal. The electrical stimulation generated from the circuit is conveyed to the target region by the electrodes wire-connected with the circuit. The devices are powered by a large battery, and controlled by a microcontroller unit. While it represents a simple approach, it is subject to various potential risks including short operation time, infection at the wound, mechanical failure of the device, and animals being hindered to move naturally, etc. A neural stimulator that is miniaturized, fully implantable, low-powered, and capable of wireless communication is required. In this dissertation, a fully implantable stimulator with remote controllability, compact size, and minimal power consumption is suggested for freely moving animal application. The stimulator consists of modular units of surface-type and depth-type arrays for accessing target brain area, package for accommodating the stimulating electronics all of which are assembled after independent fabrication and implantation using customized flat cables and connectors. The electronics in the package contains ZigBee telemetry for low-power wireless communication, inductive link for recharging lithium battery, and an ASIC that generates biphasic pulse for neural stimulation. A dual-mode power-saving scheme with a duty cycling was applied to minimize the power consumption. All modules were packaged using liquid crystal polymer (LCP) to avoid any chemical reaction after implantation. To evaluate the fabricated stimulator, wireless operation test was conducted. Signal-to-Noise Ratio (SNR) of the ZigBee telemetry were measured, and its communication range and data streaming capacity were tested. The amount of power delivered during the charging session depending on the coil distance was measured. After the evaluation of the device functionality, the stimulator was implanted into rats to train the animals to turn to the left (or right) following a directional cue applied to the barrel cortex. Functionality of the device was also demonstrated in a three-dimensional maze structure, by guiding the rats to navigate better in the maze. Finally, several aspects of the fabricated device were discussed further.λ³Έ μ—°κ΅¬μ—μ„œλŠ” μ†Œν˜• λ™λ¬Όμ˜ λ‘λ‡Œλ₯Ό μžκ·Ήν•˜κΈ° μœ„ν•œ μ™„μ „ μ΄μ‹ν˜• μ‹ κ²½μžκ·ΉκΈ°κ°€ κ°œλ°œλ˜μ—ˆλ‹€. μ†Œν˜• λ™λ¬Όμ˜ μ „κΈ°μžκ·Ήμ€ μ „μž„μƒ 연ꡬ, μ‹ κ²½κ³Όν•™ 연ꡬλ₯Ό μœ„ν•œ 행동연ꡬ 등에 ν™œμš©λœλ‹€. 특히, 자유둭게 μ›€μ§μ΄λŠ” 동물을 λŒ€μƒμœΌλ‘œ ν•œ 행동 μ—°κ΅¬λŠ” μžκ·Ήμ— μ˜ν•œ 감각 및 μš΄λ™ κΈ°λŠ₯의 μ‘°μ ˆμ„ κ΄€μ°°ν•˜λŠ” 데 μœ μš©ν•˜κ²Œ ν™œμš©λœλ‹€. 행동 μ—°κ΅¬λŠ” λ‘λ‡Œμ˜ νŠΉμ • 관심 μ˜μ—­μ„ μ§μ ‘μ μœΌλ‘œ μžκ·Ήν•˜μ—¬ λ™λ¬Όμ˜ ν–‰λ™λ°˜μ‘μ„ μ‘°κ±΄ν™”ν•˜λŠ” λ°©μ‹μœΌλ‘œ μˆ˜ν–‰λœλ‹€. μ΄λŸ¬ν•œ μ μš©μ„ κ°€λŠ₯μΌ€ ν•˜λŠ” ν•΅μ‹¬κΈ°μˆ μ€ μ΄μ‹ν˜• μ‹ κ²½μžκ·ΉκΈ°μ˜ κ°œλ°œμ΄λ‹€. μ΄μ‹ν˜• μ‹ κ²½μžκ·ΉκΈ°λŠ” λ™λ¬Όμ˜ μ›€μ§μž„μ„ λ°©ν•΄ν•˜μ§€ μ•ŠμœΌλ©΄μ„œλ„ κ·Έ 행동을 μ‘°μ ˆν•˜κΈ° μœ„ν•΄ μ‚¬μš©λœλ‹€. λ”°λΌμ„œ 동물 λ‚΄μ—μ„œμ˜ μ•ˆμ •μ μΈ λ™μž‘κ³Ό μž₯치의 크기가 μ΄μ‹ν˜• μ‹ κ²½μžκ·ΉκΈ°λ₯Ό 섀계함에 μžˆμ–΄ μ€‘μš”ν•œ λ¬Έμ œμ΄λ‹€. 기쑴의 μ‹ κ²½μžκ·ΉκΈ°λŠ” λ‘λ‡Œμ— μ΄μ‹λ˜λŠ” μ „κ·Ή λΆ€λΆ„κ³Ό, λ™λ¬Όμ˜ λ“± 뢀뢄에 μœ„μΉ˜ν•œ νšŒλ‘œλΆ€λΆ„μœΌλ‘œ κ΅¬μ„±λœλ‹€. νšŒλ‘œμ—μ„œ μƒμ‚°λœ μ „κΈ°μžκ·Ήμ€ νšŒλ‘œμ™€ μ „μ„ μœΌλ‘œ μ—°κ²°λœ 전극을 톡해 λͺ©ν‘œ μ§€μ μœΌλ‘œ μ „λ‹¬λœλ‹€. μž₯μΉ˜λŠ” 배터리에 μ˜ν•΄ κ΅¬λ™λ˜λ©°, λ‚΄μž₯된 마이크둜 μ»¨νŠΈλ‘€λŸ¬μ— μ˜ν•΄ μ œμ–΄λœλ‹€. μ΄λŠ” 쉽고 κ°„λ‹¨ν•œ μ ‘κ·Όλ°©μ‹μ΄μ§€λ§Œ, 짧은 λ™μž‘μ‹œκ°„, μ΄μ‹λΆ€μœ„μ˜ κ°μ—Όμ΄λ‚˜ μž₯치의 기계적 결함, 그리고 λ™λ¬Όμ˜ μžμ—°μŠ€λŸ¬μš΄ μ›€μ§μž„ λ°©ν•΄ λ“± μ—¬λŸ¬ λ¬Έμ œμ μ„ μ•ΌκΈ°ν•  수 μžˆλ‹€. μ΄λŸ¬ν•œ 문제의 κ°œμ„ μ„ μœ„ν•΄ 무선톡신이 κ°€λŠ₯ν•˜κ³ , μ €μ „λ ₯, μ†Œν˜•ν™”λœ μ™„μ „ μ΄μ‹ν˜• μ‹ κ²½μžκ·ΉκΈ°μ˜ 섀계가 ν•„μš”ν•˜λ‹€. λ³Έ μ—°κ΅¬μ—μ„œλŠ” 자유둭게 μ›€μ§μ΄λŠ” 동물에 μ μš©ν•˜κΈ° μœ„ν•˜μ—¬ 원격 μ œμ–΄κ°€ κ°€λŠ₯ν•˜λ©°, 크기가 μž‘κ³ , μ†Œλͺ¨μ „λ ₯이 μ΅œμ†Œν™”λœ μ™„μ „μ΄μ‹ν˜• 자극기λ₯Ό μ œμ‹œν•œλ‹€. μ„€κ³„λœ μ‹ κ²½μžκ·ΉκΈ°λŠ” λͺ©ν‘œλ‘œ ν•˜λŠ” λ‘λ‡Œ μ˜μ—­μ— μ ‘κ·Όν•  수 μžˆλŠ” ν‘œλ©΄ν˜• μ „κ·Ήκ³Ό νƒμΉ¨ν˜• μ „κ·Ή, 그리고 자극 νŽ„μŠ€ 생성 회둜λ₯Ό ν¬ν•¨ν•˜λŠ” νŒ¨ν‚€μ§€ λ“±μ˜ λͺ¨λ“ˆλ“€λ‘œ κ΅¬μ„±λ˜λ©°, 각각의 λͺ¨λ“ˆμ€ λ…λ¦½μ μœΌλ‘œ μ œμž‘λ˜μ–΄ 동물에 μ΄μ‹λœ λ’€ 케이블과 컀λ„₯ν„°λ‘œ μ—°κ²°λœλ‹€. νŒ¨ν‚€μ§€ λ‚΄λΆ€μ˜ νšŒλ‘œλŠ” μ €μ „λ ₯ 무선톡신을 μœ„ν•œ 지그비 νŠΈλžœμ‹œλ²„, 리튬 λ°°ν„°λ¦¬μ˜ μž¬μΆ©μ „μ„ μœ„ν•œ μΈλ•ν‹°λΈŒ 링크, 그리고 μ‹ κ²½μžκ·Ήμ„ μœ„ν•œ 이상성 μžκ·ΉνŒŒν˜•μ„ μƒμ„±ν•˜λŠ” ASIC으둜 κ΅¬μ„±λœλ‹€. μ „λ ₯ μ ˆκ°μ„ μœ„ν•΄ 두 개의 λͺ¨λ“œλ₯Ό 톡해 μ‚¬μš©λ₯ μ„ μ‘°μ ˆν•˜λŠ” 방식이 μž₯μΉ˜μ— μ μš©λœλ‹€. λͺ¨λ“  λͺ¨λ“ˆλ“€μ€ 이식 ν›„μ˜ 생물학적, 화학적 μ•ˆμ •μ„±μ„ μœ„ν•΄ μ•‘μ • 폴리머둜 νŒ¨ν‚€μ§•λ˜μ—ˆλ‹€. μ œμž‘λœ μ‹ κ²½μžκ·ΉκΈ°λ₯Ό ν‰κ°€ν•˜κΈ° μœ„ν•΄ 무선 λ™μž‘ ν…ŒμŠ€νŠΈκ°€ μˆ˜ν–‰λ˜μ—ˆλ‹€. 지그비 ν†΅μ‹ μ˜ μ‹ ν˜Έ λŒ€ μž‘μŒλΉ„κ°€ μΈ‘μ •λ˜μ—ˆμœΌλ©°, ν•΄λ‹Ή ν†΅μ‹ μ˜ λ™μž‘κ±°λ¦¬ 및 데이터 슀트리밍 μ„±λŠ₯이 κ²€μ‚¬λ˜μ—ˆκ³ , μž₯치의 좩전이 μˆ˜ν–‰λ  λ•Œ μ½”μΌκ°„μ˜ 거리에 따라 μ „μ†‘λ˜λŠ” μ „λ ₯의 크기가 μΈ‘μ •λ˜μ—ˆλ‹€. μž₯치의 평가 이후, μ‹ κ²½μžκ·ΉκΈ°λŠ” μ₯μ— μ΄μ‹λ˜μ—ˆμœΌλ©°, ν•΄λ‹Ή 동물은 μ΄μ‹λœ μž₯치λ₯Ό μ΄μš©ν•΄ λ°©ν–₯ μ‹ ν˜Έμ— 따라 쒌우둜 μ΄λ™ν•˜λ„λ‘ ν›ˆλ ¨λ˜μ—ˆλ‹€. λ˜ν•œ, 3차원 미둜 κ΅¬μ‘°μ—μ„œ μ₯μ˜ 이동방ν–₯을 μœ λ„ν•˜λŠ” μ‹€ν—˜μ„ ν†΅ν•˜μ—¬ μž₯치의 κΈ°λŠ₯성을 μΆ”κ°€μ μœΌλ‘œ κ²€μ¦ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, μ œμž‘λœ μž₯치의 νŠΉμ§•μ΄ μ—¬λŸ¬ μΈ‘λ©΄μ—μ„œ μ‹¬μΈ΅μ μœΌλ‘œ λ…Όμ˜λ˜μ—ˆλ‹€.Chapter 1 : Introduction 1 1.1. Neural Interface 2 1.1.1. Concept 2 1.1.2. Major Approaches 3 1.2. Neural Stimulator for Animal Brain Stimulation 5 1.2.1. Concept 5 1.2.2. Neural Stimulator for Freely Moving Small Animal 7 1.3. Suggested Approaches 8 1.3.1. Wireless Communication 8 1.3.2. Power Management 9 1.3.2.1. Wireless Power Transmission 10 1.3.2.2. Energy Harvesting 11 1.3.3. Full implantation 14 1.3.3.1. Polymer Packaging 14 1.3.3.2. Modular Configuration 16 1.4. Objectives of This Dissertation 16 Chapter 2 : Methods 18 2.1. Overview 19 2.1.1. Circuit Description 20 2.1.1.1. Pulse Generator ASIC 21 2.1.1.2. ZigBee Transceiver 23 2.1.1.3. Inductive Link 24 2.1.1.4. Energy Harvester 25 2.1.1.5. Surrounding Circuitries 26 2.1.2. Software Description 27 2.2. Antenna Design 29 2.2.1. RF Antenna 30 2.2.1.1. Design of Monopole Antenna 31 2.2.1.2. FEM Simulation 31 2.2.2. Inductive Link 36 2.2.2.1. Design of Coil Antenna 36 2.2.2.2. FEM Simulation 38 2.3. Device Fabrication 41 2.3.1. Circuit Assembly 41 2.3.2. Packaging 42 2.3.3. Electrode, Feedthrough, Cable, and Connector 43 2.4. Evaluations 45 2.4.1. Wireless Operation Test 46 2.4.1.1. Signal-to-Noise Ratio (SNR) Measurement 46 2.4.1.2. Communication Range Test 47 2.4.1.3. Device Operation Monitoring Test 48 2.4.2. Wireless Power Transmission 49 2.4.3. Electrochemical Measurements In Vitro 50 2.4.4. Animal Testing In Vivo 52 Chapter 3 : Results 57 3.1. Fabricated System 58 3.2. Wireless Operation Test 59 3.2.1. Signal-to-Noise Ratio Measurement 59 3.2.2. Communication Range Test 61 3.2.3. Device Operation Monitoring Test 62 3.3. Wireless Power Transmission 64 3.4. Electrochemical Measurements In Vitro 65 3.5. Animal Testing In Vivo 67 Chapter 4 : Discussion 73 4.1. Comparison with Conventional Devices 74 4.2. Safety of Device Operation 76 4.2.1. Safe Electrical Stimulation 76 4.2.2. Safe Wireless Power Transmission 80 4.3. Potential Applications 84 4.4. Opportunities for Further Improvements 86 4.4.1. Weight and Size 86 4.4.2. Long-Term Reliability 93 Chapter 5 : Conclusion 96 Reference 98 Appendix - Liquid Crystal Polymer (LCP) -Based Spinal Cord Stimulator 107 κ΅­λ¬Έ 초둝 138 κ°μ‚¬μ˜ κΈ€ 140Docto

    The characteristics of sleep in headache patients

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    Dept. of Dental Science/석사[ν•œκΈ€]두톡과 수면과의 μƒκ΄€κ΄€κ³„λŠ” μ—¬λŸ¬ 연ꡬ듀에 μ˜ν•΄ λ³΄κ³ λ˜μ–΄ μ™”λ‹€. ν•˜μ§€λ§Œ 이듀 κ°„μ˜ μ •ν™•ν•œ μƒκ΄€κ΄€κ³„λŠ” 아직 λͺ…ν™•νžˆ λ°ν˜€μ§€μ§€ μ•Šμ•˜μœΌλ©° ν˜„μž¬κΉŒμ§€λ„ λ…Όλž€μ΄ κ³„μ†λ˜κ³  μžˆλ‹€. λ³Έ μ—°κ΅¬λŠ” 두톡 ν™˜μžμ˜ 수면 양상에 λŒ€ν•œ 뢄석을 ν†΅ν•˜μ—¬ 두톡과 수면과의 상관관계에 λŒ€ν•˜μ—¬ 쑰사 ν•˜λŠ” 것을 λͺ©μ μœΌλ‘œ ν•˜μ˜€λ‹€. 총 101λͺ…μ˜ 두톡 ν™˜μž 및 128λͺ…μ˜ 두톡이 μ‘΄μž¬ν•˜μ§€ μ•ŠλŠ” κ±΄κ°•ν•œ λŒ€μ‘°κ΅°μ΄ λ³Έ 연ꡬ에 μ°Έμ—¬ν•˜μ˜€λ‹€. 두톡ꡰ은 λ‘ν†΅μ˜ νŠΉμ„±μ— λŒ€ν•œ 문진 및 Migraine Disability Assessment (MIDAS) 섀문을 μ‹œν–‰ν•˜μ˜€μœΌλ©°, λͺ¨λ“  μ°Έκ°€μžλŠ” Pittsburgh Sleep Quality Index (PSQI) 와 Epworth Sleepiness Scale (ESS)λ₯Ό μ΄μš©ν•˜μ—¬ 수면의 질 및 μ£Όκ°„μ‘Έλ¦¬μ›€μ¦μ˜ 정도λ₯Ό ν‰κ°€ν•˜μ˜€λ‹€. μΆ”κ°€μ μœΌλ‘œ 두톡ꡰ과 λŒ€μ‘°κ΅° 각각 28λͺ…μ˜ μ°Έκ°€μžλ₯Ό μž„μ˜ μ„ μ •ν•˜μ—¬ κ°„μ΄μˆ˜λ©΄κ²€μ‚¬κΈ°μΈ ApneaLinkTM (Resmed Inc., Poway, California, USA)λ₯Ό μ΄μš©ν•˜μ—¬ 수면무호흑-μ €ν˜Έν‘μ§€μˆ˜ (AHI, Apnea-hypopnea index), μ‚°μ†ŒλΆˆν¬ν™”μ§€μˆ˜ (ODI, Oxygen desaturation index), μ•Όκ°„μ‚°μ†Œν¬ν™”λ„ (nocturnal oxygen saturation) 및 수면μž₯μ• ν˜Έν‘ (SDB, sleep disordered breathing)의 μœ λ³‘λ₯ μ— λŒ€ν•˜μ—¬ μ‘°μ‚¬ν•˜μ˜€λ‹€. 연ꡬ κ²°κ³ΌλŠ” λ‹€μŒκ³Ό κ°™λ‹€. 1. 두톡과 수면의 질 μ‚¬μ΄μ—λŠ” μœ μ˜ν•œ 상관관계가 κ΄€μ°°λ˜μ—ˆλ‹€. Global PSQI λŠ” λŒ€μ‘°κ΅°μ— λΉ„ν•˜μ—¬ λ‘ν†΅κ΅°μ—μ„œ ν˜„μ €νžˆ λ†’κ²Œ λ‚˜νƒ€λ‚¬μœΌλ©° (p5)의 λΉ„μœ¨ λ˜ν•œ λ‘ν†΅κ΅°μ—μ„œ ν˜„μ €νžˆ λ†’μ•˜λ‹€(p<0.0001). 2. 두톡과 주간쑸리움증 μ‚¬μ΄μ—λŠ” μœ μ˜ν•œ 상관관계가 κ΄€μ°°λ˜μ—ˆλ‹€. ESS scoresλŠ” λŒ€μ‘°κ΅°μ— λΉ„ν•˜μ—¬ λ‘ν†΅κ΅°μ—μ„œ ν˜„μ €νžˆ λ†’κ²Œ λ‚˜νƒ€λ‚¬μœΌλ©° (p10)이 μœ λ³‘λ₯  λ˜ν•œ λ‘ν†΅κ΅°μ—μ„œ ν˜„μ €νžˆ λ†’μ•˜λ‹€ (p<0.0001). 3. 수면의 μ§ˆμ€ λ‘ν†΅μ˜ 강도와 μœ μ˜ν•œ 연관성을 λ³΄μ˜€λ‹€. poor sleeper group은 good sleeper group에 λΉ„ν•˜μ—¬ 높은 NRS (p=0.0347) 및 MIDAS score (p=0.0016)λ₯Ό λ‚˜νƒ€λ‚΄μ—ˆλ‹€. 반면, 주간쑸리움증은 λ‘ν†΅μ˜ 강도와 μœ μ˜ν•  λ§Œν•œ 연관성을 보이지 μ•Šμ•˜λ‹€. 4. λ‘ν†΅μ˜ λ§Œμ„±λ„λŠ” 수면의 질 및 주간쑸리움증과 μœ μ˜ν•œ 연관성을 λ³΄μ˜€λ‹€. λ§Œμ„± 두톡ꡰ은 κΈ‰μ„± 두톡ꡰ에 λΉ„ν•˜μ—¬ 높은 global PSQI (p=0.0003) 및 μ£Όκ°„μ‘Έλ¦¬μ›€μ¦μ˜ μœ λ³‘λ₯  (p=0.0312)을 λ‚˜νƒ€λ‚΄μ—ˆλ‹€. ν•˜μ§€λ§Œ 기상 μ‹œ 두톡 (morning headache)의 쑴재 μœ λ¬΄λŠ” 수면의 질 λ˜λŠ” 주간쑸리움증과 μœ μ˜ν•  λ§Œν•œ 연관성을 보이지 μ•Šμ•˜λ‹€ 5. 두톡ꡰ과 λŒ€μ‘°κ΅°κ°„μ˜ 수면무호흑-μ €ν˜Έν‘μ§€μˆ˜, μ‚°μ†ŒλΆˆν¬ν™”μ§€μˆ˜, 수면μž₯μ• ν˜Έν‘μ˜ μœ λ³‘λ₯ , μ•Όκ°„μ‚°μ†Œν¬ν™”λ„λŠ” μœ μ˜ν• λ§Œν•œ 차이λ₯Ό 보이지 μ•Šμ•˜λ‹€. 6. 기상 μ‹œ 두톡이 μ‘΄μž¬ν•˜λŠ” κ΅°κ³Ό 그렇지 μ•Šμ€ κ΅° κ°„μ˜ 수면무호흑-μ €ν˜Έν‘μ§€μˆ˜, μ‚°μ†ŒλΆˆν¬ν™”μ§€μˆ˜, 수면μž₯μ• ν˜Έν‘μ˜ μœ λ³‘λ₯ , μ•Όκ°„μ‚°μ†Œν¬ν™”λ„λŠ” μœ μ˜ν• λ§Œν•œ 차이λ₯Ό 보이지 μ•Šμ•˜λ‹€. 상기 연ꡬ 결과에 κΈ°μ΄ˆν•˜μ˜€μ„ λ•Œ, 두톡과 수면 μ‚¬μ΄μ—λŠ” μœ μ˜ν•œ 연관성이 μ‘΄μž¬ν•˜λŠ” κ²ƒμœΌλ‘œ 보인닀. 특히 λ‘ν†΅μ˜ 강도 및 λ§Œμ„±λ„λŠ” 수면의 질과 주간쑸리움증과 μœ μ˜ν•œ 연관성을 λ‚˜νƒ€λƒˆλ‹€. ν•˜μ§€λ§Œ, λ³Έ μ—°κ΅¬μ—μ„œλŠ” 두톡과 μ•Όκ°„μ €μ‚°μ†Œμ¦ (nocturnal hypoxia) λ˜λŠ” 수면μž₯μ• ν˜Έν‘μ˜ 쑴재 유무 μ‚¬μ΄μ˜ μœ μ˜ν• λ§Œν•œ 연관성을 λ°œκ²¬ν•  수 μ—†μ—ˆλ‹€. [영문]Background: The relationship between headache and sleep has been investigated by many studies, but it remains controversial and poorly understood. Objectives: This study investigated the relationship between headache and sleep by evaluating sleep quality, daytime sleepiness, and specific features related to sleep-disordered breathing (SDB). Method: A total of 101 subjects suffering from headache and 128 healthy controls were enrolled. In order to collect information on various aspects of headache attacks, those in the headache group completed a self-reported questionnaire about the characteristics of headache attacks and the Migraine Disability Assessment (MIDAS) questionnaire. The subjective quality of sleep was evaluated in all of the subjects using the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). In addition, the following specific features of sleep were evaluated in 28 subjects selected randomly from each group: apnea-hypopnea index (AHI), prevalence of SDB, nocturnal oxygen saturation (SaO2), and oxygen desaturation index (ODI) as measured using a portable monitoring device (ApneaLinkTM, Resmed Inc., Poway, California, USA). Results: 1. Sleep quality was significantly associated with headache. The global PSQI and the prevalence of poor sleeping (global PSQI >5) were significantly higher in the headache group than in the control group (both p<0.0001). 2. Daytime sleepiness was significantly associated with headache. ESS scores and the prevalence of daytime sleepiness (ESS score >10) were significantly higher in the headache group than in the control group (both p<0.0001). 3. Sleep quality was significantly associated with headache severity. The mean scores on the numerical rating scale and the MIDAS were significantly higher in the poor-sleeper group than in the good-sleeper group (p=0.0347 and p=0.0016, respectively). However, daytime sleepiness was not significantly associated with headache severity. 4. Headache chronicity was significantly associated with sleep quality and daytime sleepiness. The global PQSI and prevalence of daytime sleepiness were significantly higher in the chronic-headache group than in the acute-headache group (p=0.0003 and p=0.0312, respectively). However, morning headache was not significantly associated with sleep quality or daytime sleepiness. 5. The AHI, ODI, prevalence of SDB, and nocturnal SaO2 did not differ significantly between the headache and control groups. 6. The AHI, ODI, prevalence of SDB, and nocturnal SaO2 did not differ significantly between the morning-headache group and no-morning-headache group. Conclusion: The obtained results indicate that there is a significant association between headache and sleep. Among various characteristics of headache, severity and chronicity were significantly associated with sleep quality and daytime sleepiness, while no statistically significant association was evident between headache and nocturnal hypoxia or SDB.ope

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