딥러닝 기반 혈압 예측 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 윤성로.While COVID-19 is changing the world's social profile, it is expected that the telemedicine sector, which has not been activated due to low regulation and reliability, will also undergo a major change. As COVID-19 spreads in the United States, the US Department of Health \& Human Services temporarily loosens the standards for telemedicine, while enabling telemedicine using Facebook, Facebook Messenger-based video chat, Hangouts, and Skype. The expansion of the telemedicine market is expected to quickly transform the existing treatment-oriented hospital-led medical market into a digital healthcare service market focused on prevention and management through wearables, big data, and health records analysis. In this prevention and management-oriented digital healthcare service, it is very important to develop a technology that can easily monitor a person's health status. One of the vital signs that can be used for personal health monitoring is blood pressure. High BP is a common and dangerous condition. About 1 out of 3 adults in the U.S. (about 75 million people) have high BP. This common condition increases the risk of heart disease and stroke, two of the leading causes of death for Americans. High BP is called the silent killer because it often has no warning signs or symptoms, and many people are not aware they have it. For these reasons, it is important to develop a technology that can easily and conveniently check BP regularly. In biomedical data analysis, various studies are being attempted to effectively analyze by applying machine learning to biomedical big data accumulated in large quantities. However, collecting blood pressure-related data at the level of big data is very difficult and very expensive because it takes a lot of manpower and time. So in this dissertation, we proposed a three-step strategy to overcome these issues. First, we describe a BP prediction model with extraction and concentration CNN architecture, to process publicly disclosed sequential ECG and PPG dataset. Second, we evaluate the performance of the developed model by applying the developed model to privately measured data. To address the third issue, we propose the knowledge distillation method and input pre-processing method to improve the accuracy of the blood pressure prediction model. All the methods proposed in this dissertation are based on a deep convolutional neural network (CNN). Unlike other studies based on manual recognition of the features, by utilizing the advantage of deep learning which automatically extracts features, raw biomedical signals are used intact to reflect the inherent characteristics of the signals themselves.코로나 19에 의한 전 세계의 사회적 프로필 변화로, 규제와 신뢰성이 낮기 때문에 활성화 되지 않은 원격 의료 분야도 큰 변화를 겪을 것으로 예상됩니다. 코로나 19가 미국에 퍼짐에 따라 미국 보건복지부는 원격 진료의 표준을 일시적으로 완화하면서 페이스북, 페이스북 메신저 기반 화상 채팅, 행아웃, 스카이프를 사용한 원격 진료를 가능하게 했습니다. 원격의료 시장의 확장은 기존의 치료중심 병원주도의 의료시장을 웨어러블, 빅 데이터 및 건강기록 분석을 통한 예방 및 관리에 중점을 둔 디지털 의료 서비스 시장으로 빠르게 변화시킬 것으로 예상됩니다. 이러한 예방 및 관리 중심의 디지털 헬스케어 서비스에서는 사람의 건강 상태를 쉽게 모니터링 할 수 있는 기술 개발이 매우 중요한데 혈압은 개인 건강 모니터링에 사용될 수 있는 필수 징후 중 하나 입니다. 고혈압은 아주 흔하고 위험한 질환입니다. 미국 성인 3명중 1명(약 7,500만명)이 고혈압을 가지고 있습니다. 이는 미국인의 주요 사망 원인 중 두가지인 심장질환과 뇌졸중의 위험을 증가 시킵니다. 고혈압은 신체에 경고 신호나 자각 증상이 없어 많은 사람들이 자신이 고혈압인 것을 인지하지 못하기 때문에 "사일런트 킬러"라 불리웁니다. 이러한 이유로 정기적으로 쉽고 편리하게 혈압을 확인할 수 있는 기술의 개발이 매우 중요합니다. 생체의학 데이터 분석 분야에서는 머신 러닝을 대량으로 수집된 생체의학 빅 데이터에 적용하는 다양한 연구가 효과적으로 이루어지고 있습니다. 그러나 빅 데이터 수준으로 다량의 혈압 관련 데이터를 수집하는 것은 많은 전문적인 인력들이 오랜시간을 필요로 하기 때문에 매우 어렵고 비용 또한 많이 필요합니다. 따라서 본 학위논문에서는 이러한 문제를 극복하기 위한 3단계 전략을 제안했습니다. 먼저 누구나 시용할 수 있도록 공개되어 있는 심전도, 광용적맥파 데이터셋을 이용, 순차적인 심전도, 광용적맥파 신호에서 혈압을 잘 예측하도록 고안된 추출 및 농축 작업을 반복하는 함성곱 신경망 구조를 제안했습니다. 두번째로 제안된 합성곱 신경망 모델을 개인에게서 측정한 광용적맥파 신호를 이용해 제안된 함성곱 신경망 모델의 성능을 평가했습니다. 세번째로 혈압예측 모델의 정확성을 높이기 위해 지식 증류법과 입력신호 전처리 방법을 제안했습니다. 이 논문에서 제안된 모든 혈압예측 방법은 합성곱 신경망을 기반으로 합니다. 혈압 예측에 필요한 특징들을 수동으로 추출해야 하는 다른 연구들과 다르게 특징을 자동으로 추출하는 딥러닝의 장점을 활용, 아무런 처리도 하지 않은 원래 그대로의 생체 신호에서 신호 자체의 고유한 특징을 반영할 수 있습니다.1 Introduction 1 2 Background 5 2.1 Cuff-based BP measurement methods 9 2.1.1 Auscultatory method 9 2.1.2 Oscillometric method 10 2.1.3 Tonometric method 11 2.2 Biomedical signals used in cuffless BP prediction methods 13 2.2.1 Electrocardiography (ECG) 13 2.2.2 Photoplethysmography (PPG) 20 2.3 Cuffless BP measurement methods 21 2.3.1 PWV based BP prediction methods 25 2.3.2 Machine learning based pulse wave analysis methods 26 2.4 Deep learning for sequential biomedical data 30 2.4.1 Convolutional neural networks 31 2.4.2 Recurrent neural networks 32 3 End-to-end blood pressure prediction via fully convolutional networks 33 3.1 Introduction 35 3.2 Method 38 3.2.1 Data preparation 38 3.2.2 CNN based prediction model 41 3.2.3 Detailed architecture 45 3.3 Experimental results 47 3.3.1 Setup 47 3.3.2 Model evaluation & selection 48 3.3.3 Calibration-based method 51 3.3.4 Performance comparison 52 3.3.5 Verification using international standards for BP measurement grading criteria 54 3.3.6 Performance comparison by the input signal combinations 56 3.3.7 An ablation study of each architectural component of extraction-concentration blocks 58 3.3.8 Preprocessing of input signal to improve blood pressure prediction performance 59 3.4 Discussion 61 3.5 Summary 63 4 Blood pressure prediction by a smartphone sensor using fully convolutional networks 64 4.1 Introduction 66 4.2 Method 69 4.2.1 Data acquisition 71 4.2.2 Preprocessing of the PPG signals 71 4.2.3 PPG signal selection 71 4.2.4 Data preparation for CNN model training 72 4.2.5 Network architectures 72 4.3 Experimental results 75 4.3.1 Implementation details 75 4.3.2 Effect of PPG combination on BP prediction 75 4.3.3 Performance comparison with other related works 76 4.3.4 Verification using international standards for BP measurement grading criteria 77 4.3.5 Preprocessing of input signal to improve blood pressure prediction performance 79 4.4 Discussion 81 4.5 Summary 83 5 Improving accuracy of blood pressure prediction by distilling the knowledge of neural networks 84 5.1 Introduction 85 5.2 Methods 87 5.3 Experimental results 88 5.4 Discussion & Summary 89 6 Conclusion 90 6.1 Future work 92 Bibliography 93 Abstract (In Korean) 106Docto

Predicting media literacy level of secondary school students in Fiji

The digital revolution has set a platform for all the information and means of communication to be digitised, thus creating a digital media society. This explosion of digital media requires individuals to have a set of skills and knowledge to survive in this lifelong digital media society. In such a context, many countries around the world are now leveraging on Media Literacy to enhance the necessary skills of individuals and improve upon responsible media engagement. Therefore, predicting media literacy of students is essential so that suitable interventions can be put in place. This paper presents an analysis of Media Literacy status of Year 12 and Year 13 students at randomly selected secondary schools in Fiji, and it presents a set of predictive models using classification techniques. A quantitative study using a reliable survey was conducted to determine the Media Literacy of students using a Likert scale of 1-5. The analysis for this study was using the R software whereby classification algorithms such as Random Forest Classifiers, Decision Trees and Support Vector Machine Algorithm (SVM) were used to build the predictive models. These models will be used to derive appropriate interventions to improve Media Literacy of students. The baseline data from the study provide information on media literacy of Fijian students. The paper concludes with the important attributes that contribute towards an individual's competency on media literacy

Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art

Predicting Personality Traits Using Smartphone Sensor Data and App Usage Data

Human behavior is complex -- often defying explanation using traditional mathematical models. To simplify modeling, researchers often create intermediate psychological models to capture aspects of human behavior. These intermediate forms, such as those gleaned from personality inventories, are typically validated using standard survey instruments, and often correlate with behavior. Typically these constructs are used to predict stylized aspects of behavior. Novel sensing systems have made tracking behavior possible with unprecedented fidelity, posing the question as to whether the inverse process is possible: that is, inferring psychological constructs for individuals from behavioral data. Modern smartphones contain an array of sensors which can be filtered, combined, and analyzed to provide abstract measures of human behavior. Being able to extract a personal profile or personality type from data directly obtainable from a mobile phone without participant interaction could have applications for marketing or for initiating social or health interventions. In this work, we attempt to model a particularly salient and well-established personality inventory, the Big Five framework. Daily routines of participants were measured from parameters readily available from smartphones and supervised machine learning was used to create a model from that data. Cross validation-based evaluation demonstrated that the root mean squared error was sufficiently small to make actionable predictions about a person's personality from smartphone logs, but the model performed poorly for personality outliers

Application of the Markov Chain Method in a Health Portal Recommendation System

This study produced a recommendation system that can effectively recommend items on a health portal. Toward this aim, a transaction log that records users’ traversal activities on the Medical College of Wisconsin’s HealthLink, a health portal with a subject directory, was utilized and investigated. This study proposed a mixed-method that included the transaction log analysis method, the Markov chain analysis method, and the inferential analysis method. The transaction log analysis method was applied to extract users’ traversal activities from the log. The Markov chain analysis method was adopted to model users’ traversal activities and then generate recommendation lists for topics, articles, and Q&A items on the health portal. The inferential analysis method was applied to test whether there are any correlations between recommendation lists generated by the proposed recommendation system and recommendation lists ranked by experts. The topics selected for this study are Infections, the Heart, and Cancer. These three topics were the three most viewed topics in the portal. The findings of this study revealed the consistency between the recommendation lists generated from the proposed system and the lists ranked by experts. At the topic level, two topic recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while one topic recommendation list was highly consistent with the list ranked by experts. At the article level, one article recommendation list generated from the proposed system was consistent with the list ranked by experts, while 14 article recommendation lists were highly consistent with the lists ranked by experts. At the Q&A item level, three Q&A item recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while 12 Q&A item recommendation lists were highly consistent with the lists ranked by experts. The findings demonstrated the significance of users’ traversal data extracted from the transaction log. The methodology applied in this study proposed a systematic approach to generating the recommendation systems for other similar portals. The outcomes of this study can facilitate users’ navigation, and provide a new method for building a recommendation system that recommends items at three levels: the topic level, the article level, and the Q&A item level

Identification of Affective States in MOOCs: A Systematic Literature Review

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent