Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

A novel method for the continual, cuff-less estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values based on signal complexity analysis of the photoplethysmogram (PPG) and the electrocardiogram (ECG) is reported. The proposed framework estimates the blood pressure (BP) values obtained from signals generated from 14 volunteers subjected to a series of exercise routines. Herein, the physiological signals were first pre-processed, followed by the extraction of complexity features from both the PPG and ECG. Subsequently the complexity features were used in regression models (artificial neural network (ANN), support vector machine (SVM) and LASSO) to predict the BP. The performance of the approach was evaluated by calculating the mean absolute error and the standard deviation of the predicted results and compared with the recommendations made by the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation. Complexity features from the ECG and PPG were investigated independently, along with the combined dataset. It was observed that the complexity features obtained from the combination of ECG and PPG signals resulted to an improved estimation accuracy for the BP. The most accurate DBP result of 5.15 ± 6.46 mmHg was obtained from ANN model, and SVM generated the most accurate prediction for the SBP which was estimated as 7.33 ± 9.53 mmHg. Results for DBP fall within recommended performance of the BHS but SBP is outside the range. Although initial results are promising, further improvements are required before the potential of this approach is fully realised

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010-2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology

대규모 인구 모델과 단일 가슴 착용형 장치를 활용한 비침습적 연속 동맥 혈압 모니터링 시스템

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.최근 수십 년 동안 비침습적 연속 혈압 모니터링에 대한 필요성이 점차 대두되면서 맥파 전달 시간, 맥파 도달 시간, 또는 광용적맥파의 파형으로부터 추출된 다양한 특징들을 이용한 혈압 추정 연구들이 전세계적으로 활발하게 진행되었다. 하지만 대부분의 연구들은 국제 혈압 표준을 만족시키지 못하는 매우 적은 수의 피험자들 만을 대상으로 주로 혈압 추정 모델을 개발 및 검증하였기 때문에 성능의 정확도가 적절하게 검증되지 못했다는 한계점이 있었고, 또한 혈압 추정 파라미터 추출을 위한 생체 신호들을 측정하기 위해 대부분 두 개 이상의 모듈을 필요로 하면서 실용성 측면에서 한계점이 있었다. 첫 번째 연구는 대규모 생체신호 데이터베이스들을 분석함으로써 임상적으로 허용 가능한 수준의 정확도가 적절히 검증된 혈압 추정 모델을 개발하는 것을 목적으로 진행되었다. 본 연구에서는 1376명의 수술 중 환자들의 약 250만 심박 주기에 대해 측정된 두 가지 비침습적 생체신호인 심전도와 광용적맥파를 활용한 혈압 추정 방식들을 분석하였다. 맥파 도달 시간, 심박수, 그리고 다양한 광용적맥파 파형 피처들을 포함하는 총 42 종류의 파라미터들을 대상으로 피처 선택 기법들을 적용한 결과, 28개의 피처들이 혈압 추정 파라미터로 결정되었고, 특히 두 가지 광용적맥파 피처들이 기존에 혈압 추정 파라미터로 가장 주요하게 활용되었던 맥파 도달 시간보다 우월한 파라미터들로 분석되었다. 선정된 파라미터들을 활용하여 혈압의 낮은 주파수 성분을 인공신경망으로 모델링하고, 높은 주파수 성분을 순환신경망으로 모델링 한 결과, 수축기 혈압 에러율 0.05 ± 6.92 mmHg와 이완기 혈압 에러율 -0.05 ± 3.99 mmHg 정도의 높은 정확도를 달성하였다. 또 다른 생체신호 데이터베이스에서 추출한 334명의 중환자들을 대상으로 모델을 외부 검증했을 때 유사한 결과를 획득하면서 세 가지 대표적 혈압 측정 장비 기준들을 모두 만족시켰다. 해당 결과를 통해 제안된 혈압 추정 모델이 1000명 이상의 다양한 피험자들을 대상으로 적용 가능함을 확인하였다. 두 번째 연구는 일상 생활 중 장기간 모니터링이 가능한 단일 착용형 혈압 모니터링 시스템을 개발하는 것을 목적으로 진행되었다. 대부분의 기존 혈압 추정 연구들은 혈압 추정 파라미터 추출을 위해 필요한 생체신호들을 측정하기 위해 두 군데 이상의 신체 지점에 두 개 이상의 모듈을 부착하는 등 실용성 측면에서 한계를 나타냈다. 이를 해결하기 위해 본 연구에서는 심전도와 광용적맥파를 동시에 연속적으로 측정하는 단일 가슴 착용형 디바이스를 개발하였고, 개발된 디바이스를 대상으로 총 25명의 건강한 피험자들로부터 데이터를 획득하였다. 손가락에서 측정된 광용적맥파와 가슴에서 측정된 광용적맥파 간 파형의 특성에 유의미한 차이가 있기 때문에 가슴에서 측정된 광용적맥파에서 추출된 피처들을 대응되는 손가락에서 측정된 광용적맥파 피처들로 특성을 변환하는 전달 함수 모델을 개발하였다. 25명으로부터 획득한 데이터에 전달 함수 모델을 적용시킨 후 혈압 추정 모델을 검증한 결과, 수축기 혈압 에러율 0.54 ± 7.47 mmHg와 이완기 혈압 에러율 0.29 ± 4.33 mmHg로 나타나면서 세 가지 혈압 측정 장비 기준들을 모두 만족시켰다. 결론적으로 본 연구에서는 임상적으로 허용 가능한 수준의 정확도로 장기간 일상 생활이 가능한 비침습적 연속 동맥 혈압 모니터링 시스템을 개발하고 다수의 데이터셋을 대상으로 검증함으로써 고혈압 조기 진단 및 예방을 위한 모바일 헬스케어 서비스의 가능성을 확인하였다.As non-invasive continuous blood pressure monitoring (NCBPM) has gained wide attraction in the recent decades, many studies on blood pressure (BP) estimation using pulse transit time (PTT), pulse arrival time (PAT), and characteristics extracted from the morphology of photoplethysmogram (PPG) waveform as indicators of BP have been conducted. However, most of the studies have used small homogeneous subject pools to generate models of BP, which led to inconsistent results in terms of accuracy. Furthermore, the previously proposed modalities to measure BP indicators are questionable in terms of practicality, and lack the potential for being utilized in daily life. The first goal of this thesis is to develop a BP estimation model with clinically valid accuracy using a large pool of heterogeneous subjects undergoing various surgeries. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and PPG, from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate, and various PPG morphology features. BP estimation models were constructed using linear regression, random forest, artificial neural network (ANN), and recurrent neural network (RNN), and the performances were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ± 6.92 mmHg for systolic blood pressure (SBP), and -0.05 ± 3.99 mmHg for diastolic blood pressure (DBP) were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three international standards concerning the accuracy of BP monitors. The results indicate that the proposed method can be applied to large number of subjects and various subject phenotypes. The second goal of this thesis is to develop a wearable BP monitoring system, which facilitates NCBPM in daily life. Most previous studies used two or more modules with bulky electrodes to measure biosignals such as ECG and PPG for extracting BP indicators. In this study, a single wireless chest-worn device measuring ECG and PPG simultaneously was developed. Biosignal data from 25 healthy subjects measured by the developed device were acquired, and the BP estimation model developed above was tested on this data after applying a transfer function mapping the chest PPG morphology features to the corresponding finger PPG morphology features. The model yielded mean errors of 0.54 ± 7.47 mmHg for SBP, and 0.29 ± 4.33 mmHg for DBP, again satisfying the three standards for the accuracy of BP monitors. The results indicate that the proposed system can be a stepping stone to the realization of mobile NCBPM in daily life. In conclusion, the clinical validity of the proposed system was checked in three different datasets, and it is a practical solution to NCBPM due to its non-occlusive form as a single wearable device.Abstract i Contents iv List of Tables vii List of Figures viii Chapter 1 General Introduction 1 1.1 Need for Non-invasive Continuous Blood Pressure Monitoring (NCBPM) 2 1.2 Previous Studies for NCBPM 5 1.3 Issues with Previous Studies 9 1.4 Thesis Objectives 12 Chapter 2 Non-invasive Continuous Arterial Blood Pressure Estimation Model in Large Population 14 2.1 Introduction 15 2.1.1 Electrocardiogram (ECG) and Photoplethysmogram (PPG) Features for Blood Pressure (BP) Estimation 15 2.1.2 Description of Surgical Biosignal Databases 16 2.2 Feature Analysis 19 2.2.1 Data Acquisition and Data Pre-processing 19 2.2.2 Feature Extraction 25 2.2.3 Feature Selection 35 2.3 Construction of the BP Estimation Models 44 2.3.1 Frequency Component Separation 44 2.3.2 Modelling Algorithms 47 2.3.3 Summary of Training and Validation 52 2.4 Results and Discussion 54 2.4.1 Feature Analysis 54 2.4.1.1 Pulse Arrival Time versus Pulse Transit Time 54 2.4.1.2 Feature Selection 57 2.4.2 Optimization of the BP Estimation Models 63 2.4.2.1 Frequency Component Separation 63 2.4.2.2 Modelling Algorithms 66 2.4.2.3 Comparison against Different Modelling Settings 68 2.4.3 Performance of the Best-case BP Estimation Model 69 2.4.4 Limitations 75 2.5 Conclusion 78 Chapter 3 Development of the Single Chest-worn Device for Non-invasive Continuous Arterial Blood Pressure Monitoring 80 3.1 Introduction 81 3.2 Development of the Single Chest-worn Device 84 3.2.1 Hardware Development 84 3.2.2 Software Development 90 3.2.3 Clinical Trial 92 3.3 Development of the Transfer Function 95 3.3.1 Finger PPG versus Chest PPG 95 3.3.2 The Concept of the Transfer Function 97 3.3.3 Data Acquisition for Modelling of the Transfer Function 98 3.4 Results and Discussion 100 3.4.1 Construction of the Transfer Function 100 3.4.2 Test of the BP Estimation Model 101 3.4.3 Comparison with the Previous Study using the Single Chest-worn Device 104 3.4.4 Limitations 106 3.5 Conclusion 108 Chapter 4 Thesis Summary and Future Direction 109 4.1 Summary and Contributions 110 4.2 Future Work 113 Bibliography 115 Abstract in Korean 129 Acknowledgement 132Docto

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

Cuffless Blood Pressure Estimation

The blood pressure is an important factor in the diagnosis and evaluation of several diseases, such as acute myocardial infarction and stroke. This way, continuous monitorization of this parameter is crucial to a correct health evaluation. The current methods, like the oscillometric method, have some major drawbacks, that can influence the output values or even make the measurements impossible. One example is the high frequency evaluation of the blood pressure, in the standard used methods the process of measuring can take up to 3 minutes, and a waiting time is necessary between consecutive measurements. This dissertation presents two different cuffless solution to solve those problems. One based on physical models of the human body, and the other using machine learning techniques. In the first solution seven models that correlate pulse transit time and blood pressure, deducted by different authors, were tested to evaluate which one performed better. The testes were performed in a custom dataset acquired at Fraunhofer AICOS and in clinical environment, with two different devices (low cost device and medical grade device). The results indicate that pulse transit time can be used to track blood pressure, the developed device/method was evaluated as grade A based in the Standard IEEE 1708-2014. The second solution it’s a proof of concept using a public database and three different machine learning methods (Random Forest, Neural Network and AdaBoost). Two sets of features are calculated from the ECG and PPG signals, one using TSFEL (spectral, frequency and time domain features) and a total of 15 custom features. The proposed method outperforms the methods presented in bibliography with mean absolute error of 3.6 mmHg and 2.0 mmHg to systolic and diastolic blood pressure respectively

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

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