Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

A computational model based on human performance for fluid management in critical care

Computational simulation is one of the most important ways of reproducing the dynamic responses of a Cyber Physical System using a model of the system. The simulation discovers areas of differential system performance and allows linking such performance back to system characteristics. In the medical domain, patient simulators are used to train physicians in patient management. One critical question is how to verify these systems under realistic human (physician) input. This requires the creation of realistic human models that would be able to capture human cognitive and decision abilities and limitations. Verification of such an overall physician-patient model would result in two advantages: (a) since physicians realistically would not give all possible inputs to the system, verification could be more efficient and (b) the verification may uncover areas of poor human performance. In this paper, we describe our methodology and results in creating a computational model of human fluid management in critical care, based on human experiments

A bibliography on parallel and vector numerical algorithms

This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also

A Graph Rewriting Approach for Transformational Design of Digital Systems

Transformational design integrates design and verification. It combines “correctness by construction” and design creativity by the use of pre-proven behaviour preserving transformations as design steps. The formal aspects of this methodology are hidden in the transformations. A constraint is the availability of a design representation with a compositional formal semantics. Graph representations are useful design representations because of their visualisation of design information. In this paper graph rewriting theory, as developed in the last twenty years in mathematics, is shown to be a useful basis for a formal framework for transformational design. The semantic aspects of graphs which are no part of graph rewriting theory are included by the use of attributed graphs. The used attribute algebra, table algebra, is a relation algebra derived from database theory. The combination of graph rewriting, table algebra and transformational design is new

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

The specification and verification of systolic wave algorithms

Bibliography: leaf 12."March, 1984""DAAG29-84-K0005" "N00014-81-K-0742"C.J. Kuo, Bernard C. Levy, Bruce R. Musicus

Noninvasive Arterial Blood Pressure Estimation using ABPNet and VITAL-ECG

4noArterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention and detection of cardiovascular diseases, which represent one of the leading causes of death in the world. Currently, the most common adopted noninvasive blood pressure measurement system is sphygmomanometer, which works by inflating and deflating a cuff around the arm. This work presents ABPNet, a new prediction technique, based on a multilayer perceptron (MLP), which uses ECG and PPG to estimate both systolic and diastolic blood pressure. To train the neural network, signals are gathered from the Physionet MIMIC database. The proposed architecture performances are evaluated w.r.t. both the invasive blood pressure signal and the noninvasive sphygmomanometer measurements. The experimental results are quite promising; they are compliant with the ANSI/AAMI/ ISO 81060-2:2013 for sphygmomanometer certification because the network predicted values are within +/-5 mmHg w.r.t. real invasive measurements, as imposed by the legislation. Finally, it is shown how ABPNet can be used to improve the VITAL-ECG, a wearable device designed to acquire vital parameters, such as electrocardiographic (ECG) and photoplethysmographic (PLETH/PPG) signals; indeed, by embedding the ABPNet neural network, VITAL-ECG can be upgraded to estimate, also, ABP. As a consequence, the device could be used to fight cardiovascular diseases and prevent their dangerous effects.partially_openopenPaviglianiti A.; Randazzo V.; Pasero E.; Vallan A.Paviglianiti, A.; Randazzo, V.; Pasero, E.; Vallan, A

Centre for Information Science Research Annual Report, 1987-1991

Annual reports from various departments of the AN