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

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

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

Enabling Wearable Hemodynamic Monitoring Using Multimodal Cardiomechanical Sensing Systems

Hemodynamic parameters such as blood pressure and stroke volume are instrumental to understanding the pathogenesis of cardiovascular disease. Unfortunately, the monitoring of these hemodynamic parameters is still limited to in-clinic measurements and cumbersome hardware precludes convenient, ubiquitous use. To address this burden, in this work, we explore seismocardiogram-based wearable multimodal sensing techniques to estimate blood pressure and stroke volume. First, the performance of a multimodal, wrist-worn device capable of obtaining noninvasive pulse transit time measurements is used to estimate blood pressure in an unsupervised, at-home setting. Second, the feasibility of this wrist-worn device is comprehensively evaluated in a diverse and medically underserved population over the course of several perturbations used to modulate blood pressure through different pathways. Finally, the ability of wearable signals—acquired from a custom chest-worn biosensor—to noninvasively quantify stroke volume in patients with congenital heart disease is examined in a hospital setting. Collectively, this work demonstrates the advancements necessary towards enabling noninvasive, longitudinal, and accurate measurements of these hemodynamic parameters in remote settings, which offers to improve health equity and disease monitoring in low-resource settings.Ph.D

Robust Algorithms for Unattended Monitoring of Cardiovascular Health

Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

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

Detecting Vital Signs with Wearable Wireless Sensors

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented

Haemodynamic optimization of cardiac resynchronization therapy

Heart failure carries a very poor prognosis, unless treated with the appropriate pharmacological agents which, have been evaluated in large randomized clinical trials and have demonstrated improvements in morbidity and mortality of this cohort of patients. A significant proportion of these patients develop conduction abnormalities involving both the atrioventricular node and also the specialised conduction tissue (bundle of His and Purkinje fibers) of the ventricular myocardium which is most commonly evidenced by the presence of a wide QRS, typically left bundle branch block. The net effect of these conduction abnormalities is inefficient filling and contraction of the left ventricle. The presence of these conduction abnormalities is an additional strong marker of poor prognosis. Over the last 15 years pacing treatments have been developed aimed at mitigating the conduction disease. Large scale randomized multicentre trials have repeatedly demonstrated the effectiveness of cardiac pacing, officially recognized as cardiac resynchronization therapy (CRT). This mode of pacing therapy has undoubtedly had a positive impact on both the morbidity and mortality of these patients. Despite the large advancement in the management of heart failure patients by pacing therapies, a significant proportion of patients (30%) being offered CRT are classed as non-responders. Many explanations have been put forward for the lack of response. The presence of scar at the pacing site with failure to capture or delayed capture of myocardium, too much left ventricular scar therefore minimal contractile response, incorrect pacing site due to often limited anatomical options of lead placement and insufficient programming i.e optimization, of pacemaker settings such as the AV and VV delay are just some of the suggested areas perceived to be responsible for the lack of patients’ response to cardiac resynchronization therapy. The effect of optimization of pacemaker settings is a field that has been investigated extensively in the last decade. Disappointingly, current methods of assessing the effect of optimization of pacemaker settings on several haemodynamic parameters, such as cardiac output and blood pressure, are marred with very poor reproducibility, so measurement of any effect of optimization is close to being meaningless. Moreover, detailed understanding of the effects of CRT on coronary physiology and cardiac mechanoenergetics is equally, disappointingly, lacking. In this thesis, I investigated the acute effects of cardiac resynchronization therapy and AV optimization on coronary physiology and cardiac mechanoenergetics. This was accomplished using very detailed and demanding series of invasive catheterization studies. I used novel analytical mathematical techniques, such as wave intensity analysis, which have been developed locally and this provided a unique insight of the important physiological entities defining coronary physiology and cardiac mechanics. I explored in detail the application and reliability of photoplethysmography as a tool for non-invasive optimization of the AV delay. Photoplethysmography has the potential of miniaturization and therefore implantation alongside pacemaker devices. I compared current optimization techniques (Echocardiography and ECG) of VV delay against beat-to-beat blood pressure using the Finometer device and defined the criteria that a technique requires if such a technique can be used meaningfully for the optimization of pacemaker settings both in clinical practice and in clinical trials. Finally, I investigated the impact of atrial pacing and heart rate on the optimal AV delay and attempted to characterize the mechanisms underlying any changes of the optimal AV delay under these varying patient and pacing states. In this thesis I found that optimization of AV delay of cardiac resynchronization therapy not only improved cardiac contraction and external cardiac work, but also cardiac relaxation and coronary blood flow, when compared against LBBB. I found that most of the increase in coronary blood flow occurred during diastole and that the predominant drive for this was ventricular microcirculatory suction as evidenced by the increased intracoronary diastolic backward-travelling decompression wave. I showed that non-invasive haemodynamic optimization using the plethysmograph signal of an inexpensive pulse oximeter is as reliable as using the Finometer. Appropriate processing of the oximetric signal improved the reproducibility of the optimal AV delay. The advantage of this technology is that it might be miniaturized and implanted to provide automated optimization. In this thesis I found that other commonly used modalities of VV optimization such as echocardiography and ECG lack internal validity as opposed to non-invasive haemodynamic optimization using blood pressure. This finding will encourage avoidance of internally invalid modalities, which may cause more harm than good. In this thesis I found that the sensed and paced optimal AV delays have, on average, a bigger difference than the one assumed by the device manufacturers and clinicians. As a significant proportion of patients will be atrially paced, especially during exercise, optimization during this mode of pacing is equally crucial as it is during atrial sensing. Finally, I found that the optimal AV delay decreases with increasing heart rate, and the slope of this is within the range of existing pacemaker algorithms used for rate adaptation of AV delay, strengthening the argument for the rate adaptation to be programmed on.Imperial Users Onl

A wearable blood pressure sensor using oscillometric photoplethysmography and micro accelerometers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (leaves 119-124).Monitoring arterial blood pressure (ABP) with a sensor virtually imperceptible to the wearer, for continuous periods of weeks, months, or years, could prove revolutionary in the diagnosis and treatment of chronic hypertension and heart failure, as well as a monitoring tool for convalescing individuals, and individuals in hazardous duty (such as firefighters or soldiers). To this end, a miniaturizable, non-invasive blood pressure sensor is designed and validated. A solid, coin-sized cuff-less photoplethysmography (PPG) sensor worn over a palpable artery is utilized to measure arterial blood pressure. Measurements are obtained through a modified oscillometric technique which eliminates the need for a high pressure cuff and instead, takes advantage of natural hydrostatic pressure changes caused by raising and lowering the subject's arm. In this work, the principle of hydrostatic oscillometry is first detailed. To better understand the internal mechanisms of pressure propagation within the tissue, a comprehensive non-linear finite element model of the finger base is constructed and validated using a combination of magnetic resonance imaging and experimental tissue stiffness measurements.(cont.) A prototype finger blood pressure monitor is designed and constructed in combination with a novel accelerometer-based height sensor. The 95% confidence interval for a Bland-Altman comparison between the proposed sensor's mean arterial pressure (MAP) measurements and the simultaneous Finapres MAP measurements is [+919, -283] Pa ([+6.91, -9.04] mmHg). The percent difference between the two methods is shown to be 3.0%. A method for continuous MAP measurements utilizing the sensor system is proposed and is shown to be capable of providing reliable measurements for several minutes.by Phillip Andrew Shaltis.Ph.D