Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring

Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring

Pulse transit time measured by photoplethysmography improves the accuracy of heart rate as a surrogate measure of cardiac output, stroke volume and oxygen uptake in response to graded exercise

Heart rate (HR) is a valuable and widespread measure for physical training programs, although its description of conditioning is limited to the cardiac response to exercise. More comprehensive measures of exercise adaptation include cardiac output ((Q) over dot), stroke volume (SV) and oxygen uptake ((V) over dotO(2)), but these physiological parameters can be measured only with cumbersome equipment installed in clinical settings. In this work, we explore the ability of pulse transit time (PTT) to represent a valuable pairing with HR for indirectly estimating (Q) over dot, SV and (V) over dotO(2) non-invasively. PTT was measured as the time interval between the peak of the electrocardiographic (ECG) R-wave and the onset of the photoplethysmography (PPG) waveform at the periphery (i.e. fingertip) with a portable sensor. Fifteen healthy young subjects underwent a graded incremental cycling protocol after which HR and PTT were correlated with (Q) over dot, SV and (V) over dotO(2) using linear mixed models. The addition of PTT significantly improved the modeling of (Q) over dot, SV and (V) over dotO(2) at the individual level (R-1(2) = 0.419 for SV, 0.548 for (Q) over dot, and 0.771 for (V) over dotO(2)) compared to predictive models based solely on HR (R-1(2) = 0.379 for SV, 0.503 for (Q) over dot, and 0.745 for (V) over dotO(2)). While challenges in sensitivity and artifact rejection exist, combining PTT with HR holds potential for development of novel wearable sensors that provide exercise assessment largely superior to HR monitors

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) Sensor

Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95\%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.N/

A model-based calibration method for the design of wearable and cuffless devices measuring arterial blood pressure.

Liu, Yinbo.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 74-79).Abstracts in English and Chinese.Abstract --- p.iList of Figures --- p.ivList of Tables --- p.viiiIntroduction --- p.1Chapter 1.1 --- Current status of Blood Pressure Management --- p.1Chapter 1.2 --- Current Status of Noninvasive Blood Pressure Measurement Techniques --- p.4Chapter 1.3 --- Motivations and Objectives of This Thesis --- p.9Chapter 1.4 --- Organization of This Thesis --- p.9Backgrounds --- p.11Chapter 2.1 --- Principle of the Pulse Transit Time-based Approach for BP Measurement --- p.11Chapter 2.1.1 --- General Descriptions --- p.11Chapter 2.1.2 --- Pressure Wave Propagation in Cylindrical Arteries --- p.13Chapter 2.1.3 --- Determining the PTT for BP Measurement --- p.14Chapter 2.2 --- Backgrounds for Pressure Related Elastic Properties of Artery --- p.17Chapter 2.2.1 --- Transmural Pressure and Its Components --- p.17Chapter 2.2.2 --- Volume-pressure Models --- p.19Chapter 2.2.3 --- Types and Structure of the Artery and Its Properties --- p.20Chapter 2.3 --- Literature Review on the Calibration Methods for Cuffless Blood Pressure Measurements --- p.22Chapter 2.4 --- Section Summary --- p.25Investigations on Factors Affecting PTT or BP --- p.26Chapter 3.1 --- The Effects of External Pressure --- p.26Chapter 3.1.1 --- Background --- p.26Chapter 3.1.2 --- Experimental protocol --- p.28Chapter 3.1.3 --- Analysis for the Effects of External Pressure on PTT --- p.30Chapter 3.1.4 --- Section Discussions --- p.31Chapter 3.2 --- The Effects of Hydrostatic Pressure --- p.32Chapter 3.2.1 --- Experimental protocol --- p.33Chapter 3.2.2 --- Analysis for the Effects of Hydrostatic Pressure on PTT --- p.34Chapter 3.2.3 --- Section Discussions --- p.37Chapter 3.2.4 --- Section Summary --- p.38Modeling the Effect of Hydrostatic Pressure on PTT for A Calibration Method --- p.39Chapter 4.1 --- Current Status of Hydrostatic Calibration Approaches --- p.39Chapter 4.2. --- Modeling Pulse Transit Time under the Effects of Hydrostatic Pressure for A Hydrostatic Calibration Method: --- p.40Chapter 4.2.1 --- Basic BP-PTT model --- p.40Chapter 4.2.2 --- V-P relationship Represented by a Sigmoid Curve --- p.40Chapter 4.2.3 --- Relating PTT with Hydrostatic Pressure --- p.41Chapter 4.2.4 --- Implementing the Hydrostatic Calibration Method for BP Estimation --- p.43Chapter 4.3. --- Preliminary Experiment --- p.44Chapter 4.3.1. --- Experimental Protocol and Methodology --- p.44Chapter 4.3.2. --- Experimental Analysis --- p.46Chapter 4.4. --- Section Discussions --- p.48Chapter 4.5. --- A Novel Implementation Algorithm of Hydrostatic Calibration Method for Cuffless BP Estimation --- p.49Chapter 4.6. --- Section Summary --- p.50Experimental Studies for the Hydrostatic Calibration Approach --- p.51Chapter 5.1 --- Experimental Analysis --- p.51Chapter 5.1.1 --- Experimental Protocol --- p.51Chapter 5.1.2 --- Methodology --- p.53Chapter 5.1.3 --- Preparations --- p.54Chapter 5.1.4 --- Experimental Results --- p.56Chapter 5.2 --- Section Discussions --- p.63Chapter 5.3 --- Section Summary --- p.70Conclusions and Suggestions for Future Works --- p.71Chapter 6.1 --- Conclusions --- p.71Chapter 6.2 --- Suggestions for Future Works --- p.72Reference --- p.7

Conduit Artery Photoplethysmography and its Applications in the Assessment of Hemodynamic Condition

Elektroniskā versija nesatur pielikumusPromocijas darbā ir izstrādāta maģistrālo artēriju fotopletizmogrāfijas (APPG) metode hemodinamisko parametru novērtējumam. Pretstatot referentām metodēm, demonstrēta iespēja iegūt arteriālo elasticitāti raksturojošus parametrus, izmantojot APPG signāla formas analīzi (atvasinājuma un signāla formas aproksimācijas parametri) un ar APPG iegūtu pulsa izplatīšanās ātrumu unilaterālā gultnē. Izstrādāta APPG reģistrācijas standartizācija, mērījuma laikā nodrošinot optimālo sensora piespiedienu. Šis paņēmiens validēts ārējās ietekmes (sensora piespiediens) un hemodinamisko stāvokļu (perifērā vaskulārā pretestība) izmaiņās femorālā APPG signālā, identificējot būtiskākos faktorus APPG pielietojumos. Veikta APPG validācija asinsrites fizioloģijas un preklīniskā pētījumā demonstrējot APPG potenciālu pētniecībā un diagnostikā. Izstrādāts pulsa formas parametrizācijas paņēmiens, saistot fizioloģiskās un aproksimācijas modeļa komponentes. Atslēgas vārdi: maģistrālā artērija, fotopletizmogrāfija, arteriālā elasticitāte, metodes standartizācija, pulsa formas kvantifikācija, vazomocija, sepseThe doctoral thesis features the development of a conduit artery photoplethysmography technique (APPG) for the evaluation of hemodynamic parameters. Contrasting referent methods, the work demonstrates the possibility to receive parameters characterizing the arterial stiffness by means of APPG waveform analysis (derivation and waveform approximation parameters) and APPG obtained pulse wave velocity in a unilateral vascular bed. In this work APPG standardization technique was developed providing optimal probe contact pressure conditions. It was validated by altering the external factors (probe contact pressure) and hemodynamic conditions (peripheral vascular resistance) on the femoral APPG waveform identifying the key factors in APPG applications. The APPG validation in blood circulation physiology and a pre-clinical trial was performed demonstrating APPG potential in the extension of applications. An arterial waveform parameterization was developed relating the physiological wave to approximation model components. Keywords: conduit artery, photoplethysmography, arterial stiffness, method standardization, waveform parametrization, vasomotion, sepsi

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