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 Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

[EN] The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.This research received financial support from grants PID2021-00X128525-IV0, PID2021123804OB-I00 and TED2021-129996B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana.Cano, J.; Fácila, L.; Gracia-Baena, JM.; Zangróniz, R.; Alcaraz, R.; Rieta, JJ. (2022). The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography. Biosensors. 12(5):1-14. https://doi.org/10.3390/bios1205028911412

Hybrid Wearable Signal Processing/Learning via Deep Neural Networks

Wearable technologies are gaining considerable attention in recent years as a potential post-smartphone platform with several applications of significant engineering importance. Wearable technologies are expected to become more prevalent in a variety of areas, including modern healthcare practices, robotic prosthesis control, Artificial Reality (AR) and Virtual Reality (VR) applications, Human Machine Interface/Interaction (HMI), and remote support for patients and chronically ill patients at home. The emergence of wearable technologies can be attributed to the advancement of flexible electronic materials; the availability of advanced cloud and wireless communication systems, and; the Internet of Things (IoT) coupled with high demand from the tech-savvy population and the elderly population for healthcare management. Wearable devices in the healthcare realm gather various biological signals from the human body, among which Electrocardiogram (ECG), Photoplethysmogram (PPG), and surface Electromyogram (sEMG), are the most widely non-intrusive monitored signals. Utilizing these widely used non-intrusive signals, the primary emphasis of the proposed dissertation is on the development of advanced Machine Learning (ML), in particular Deep Learning (DL), algorithms to increase the accuracy of wearable devices in specific tasks. In this context and in the first part, using ECG and PPG bio-signals, we focus on development of accurate subject-specific solutions for continuous and cuff-less Blood Pressure (BP) monitoring. More precisely, a deep learning-based framework known as BP-Net is proposed for predicting continuous upper and lower bounds of blood pressure, respectively, known as Systolic BP (SBP) and Diastolic BP (DBP). Furthermore, by capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a unified dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. In the second part, we focus on hand gesture recognition utilizing sEMG signals, which have the potential to be used in the myoelectric prostheses control systems or decoding Myo Armbands data to interpret human intent in AR/VR environments. Capitalizing on the recent advances in hybrid architectures and Transformers in different applications, we aim to enhance the accuracy of sEMG-based hand gesture recognition by introducing a hybrid architecture based on Transformers, referred to as the Transformer for Hand Gesture Recognition (TraHGR). In particular, the TraHGR architecture consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. The ultimate goal of this work is to increase the accuracy of gesture classifications, which could be a major step towards the development of more advanced HMI systems that can improve the quality of life for people with disabilities or enhance the user experience in AR/VR applications. Besides improving accuracy, decreasing the number of parameters in the Deep Neural Network (DNN) architectures plays an important role in wearable devices. In other words, to achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. Therefore, in our next attempt, we propose a lightweight hybrid architecture based on the Convolutional Neural Network (CNN) and attention mechanism, referred to as Hierarchical Depth-wise Convolution along with the Attention Mechanism (HDCAM), to effectively extract local and global representations of the input. The key objective behind the design of HDCAM was to ensure its resource efficiency while maintaining comparable or better performance than the current state-of-the-art methods

A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation

Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial Blood Pressure (ABP) signals as dependent variables, and then using machine learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR), and Heart Rate (HR) features from Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster. The method was implemented using the MIMICII dataset with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multi-trend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster, and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36)