Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks

Cardiovascular diseases are one of the most severe causes of mortality, taking a heavy toll of lives annually throughout the world. The continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, bringing about several layers of complexities. This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals. In addition we explore the advantage of deep learning as it would free us from sticking to ideally shaped PPG signals only, by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present, PPG2ABP, a deep learning based method, that manages to predict the continuous ABP waveform from the input PPG signal, with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of DBP, MAP and SBP from the predicted ABP waveform outperforms the existing works under several metrics, despite that PPG2ABP is not explicitly trained to do so

A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms

Background and objectives: Continuous and non-invasive blood pressure monitoring would revolutionize healthcare. Currently, blood pressure (BP) can only be accurately monitored using obtrusive cuff-based devices or invasive intra-arterial monitoring. In this work, we propose a novel hybrid neural network for the accurate estimation of blood pressure (BP) using only non-invasive electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms as inputs. Methods: This work proposes a hybrid neural network combines the feature detection abilities of temporal convolutional layers with the strong performance on sequential data offered by long short-term memory layers. Raw electrocardiogram and photoplethysmogram waveforms are concatenated and used as network inputs. The network was developed using the TensorFlow framework. Our scheme is analysed and compared to the literature in terms of well known standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Results: Our scheme achieves extremely low mean absolute errors (MAEs) of 4.41 mmHg for SBP, 2.91 mmHg for DBP, and 2.77 mmHg for MAP. A strong level of agreement between our scheme and the gold-standard intra-arterial monitoring is shown through Bland Altman and regression plots. Additionally, the standard for BP devices established by AAMI is met by our scheme. We also achieve a grade of 'A' based on the criteria outlined by the BHS protocol for BP devices. Conclusions: Our CNN-LSTM network outperforms current state-of-the-art schemes for non-invasive BP measurement from PPG and ECG waveforms. These results provide an effective machine learning approach that could readily be implemented into non-invasive wearable devices for use in continuous clinical and at-home monitoring

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

Cuffless bood pressure estimation

L'hypertension est une maladie qui affecte plus d'un milliard de personnes dans le monde. Il s'agit d'une des principales causes de décès; le suivi et la gestion de cette maladie sont donc cruciaux. La technologie de mesure de la pression artérielle la plus répandue, utilisant le brassard pressurisé, ne permet cependant pas un suivi en continu de la pression, ce qui limite l'étendue de son utilisation. Ces obstacles pourraient être surmontés par la mesure indirecte de la pression par l'entremise de l'électrocardiographie ou de la photopléthysmographie, qui se prêtent à la création d'appareils portables, confortables et peu coûteux. Ce travail de recherche, réalisé en collaboration avec le département d'ingénierie biomédicale de l'université de Lund, en Suède, porte principalement sur la base de données publique Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Datasetde PhysioNet, largement utilisée dans la littérature portant sur le développement et la validation d'algorithmes d'estimation de la pression artérielle sans brassard pressurisé. Puisque ces données proviennent d'unités de soins intensifs et ont été recueillies dans des conditions non contrôlées, plusieurs chercheurs ont avancé que les modèles d'estimation de la pression artérielle se basant sur ces données ne sont pas valides pour la population générale. Pour la première fois dans la littérature, cette hypothèse est ici mise à l'épreuve en comparant les données de MIMIC à un ensemble de données de référence plus représentatif de la population générale et recueilli selon une procédure expérimentale bien définie. Des tests statistiques révèlent une différence significative entre les ensembles de données, ainsi qu'une réponse différente aux changements de pression artérielle, et ce, pour la majorité des caractéristiques extraites du photopléthysmogramme. De plus, les répercussions de ces différences sont démontrées à l'aide d'un test pratique d'estimation de la pression artérielle par apprentissage machine. En effet, un modèle entraîné sur l'un des ensembles de données perd en grande partie sa capacité prédictive lorsque validé sur l'autre ensemble, par rapport à sa performance en validation croisée sur l'ensemble d'entraînement. Ces résultats constituent les contributions principales de ce travail et ont été soumis sous forme d'article à la revue Physiological Measurement. Un volet additionnel de la recherche portant sur l'analyse du pouls par décomposition (pulse de composition analysis ou PDA) est présenté dans un deuxième temps. La PDA est une technique permettant de séparer l'onde du pouls en une composante excitative et ses réflexions, utilisée pour extraire des caractéristiques du signal dans le contexte de l'estimation de la pression artérielle. Les résultats obtenus démontrent que l'estimation de la position temporelle des réflexions à partir de points de référence de la dérivée seconde du signal donne d'aussi bons résultats que leur détermination par la méthode traditionnelle d'approximation successive, tout en étant beaucoup plus rapide. Une méthode récursive rapide de PDA est également étudiée, mais démontrée comme inadéquate dans un contexte de comparaison intersujet.Hypertension affects more than one billion people worldwide. As one of the leading causes of death, tracking and management of the condition is critical, but is impeded by the current cuff-based blood pressure monitoring technology. Continuous and more ubiquitous blood pressure monitoring may be achieved through simpler, cheaper and less invasive cuff-less devices, performing an indirect measure through electrocardiography or photoplethysmography. Produced in collaboration with the department of biomedical engineering of Lund Universityin Sweden, this work focuses on public data that has been widely used in the literature to develop and validate cuffless blood pressure estimation algorithms: The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Dataset from PhysioNet. Because it is sourced from intensive care units and collected in absence of controlled conditions, it has many times been hypothesized that blood pressure estimation models based on its data may not generalize to the normal population. This work tests that hypothesis for the first time by comparing the MIMIC dataset to another reference dataset more representative of the general population and obtained under controlled experimental conditions. Through statistical testing, a majority of photoplethysmogram based features extracted from MIMIC are shown to differ significantly from the reference dataset and to respond differently to blood pressure changes. In addition, the practical impact of those differences is tested through the training and cross validating of machine learning models on both datasets, demonstrating an acute loss of predictive powers of models facing data from outside the dataset used in the training phase. As the main contribution of this work, these findings have been submitted as a journal paper to Physiological Measurement. Additional original research is also presented in relation to pulse decomposition analysis (PDA), a technique used to separate the pulse wave from its reflections, in the context of blood pressure estimation. The results obtained through this work show that when using the timing of reflections as part of blood pressure predictors, estimating those timings from fiducial points in the second derivative works as well as using the traditional and computationally costly successive approximation PDA method, while being many times faster. An alternative fast recursive PDA algorithm is also presented and shown to perform inadequately in an inter-subject comparison context

A Review of Deep Learning Methods for Photoplethysmography Data

Photoplethysmography (PPG) is a highly promising device due to its advantages in portability, user-friendly operation, and non-invasive capabilities to measure a wide range of physiological information. Recent advancements in deep learning have demonstrated remarkable outcomes by leveraging PPG signals for tasks related to personal health management and other multifaceted applications. In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed from three key perspectives: tasks, models, and data. We finally extracted 193 papers where different deep learning frameworks were used to process PPG signals. Based on the tasks addressed in these papers, we categorized them into two major groups: medical-related, and non-medical-related. The medical-related tasks were further divided into seven subgroups, including blood pressure analysis, cardiovascular monitoring and diagnosis, sleep health, mental health, respiratory monitoring and analysis, blood glucose analysis, as well as others. The non-medical-related tasks were divided into four subgroups, which encompass signal processing, biometric identification, electrocardiogram reconstruction, and human activity recognition. In conclusion, significant progress has been made in the field of using deep learning methods to process PPG data recently. This allows for a more thorough exploration and utilization of the information contained in PPG signals. However, challenges remain, such as limited quantity and quality of publicly available databases, a lack of effective validation in real-world scenarios, and concerns about the interpretability, scalability, and complexity of deep learning models. Moreover, there are still emerging research areas that require further investigation

Cuffless ambulatory blood pressure measurement using the photoplethysmogram and the electrocardiogram

Blood pressure (BP), as with other vital signs such as heart rate and respiratory rate, exhibits endogenous oscillations over a period of approximately 24 hours, a phenomenon known as circadian rhythmicity. This rhythm typically reaches a nadir during sleep, however, different BP circadian rhythm phenotypes exist depending on the magnitude and direction of the nocturnal change. Analysis of these phenotypes has been shown to be an independent indicator for the onset of cardiovascular disease, the leading cause of non-communicable mortality and morbidity worldwide. However, currently the established technique for monitoring BP over 24 hours in the general population requires an inflatable cuff wrapped around the upper arm. This procedure is highly disruptive to sleep and daily life, and therefore rarely performed in primary care. Although commercial cuffless BP devices do exist, their accuracy has been questioned, and consequently, the clinical community do not recommend their use. In this thesis, I investigated techniques to measure BP in an ambulatory environment without an inflatable cuff using two signals commonly acquired by wearable sensors: the photoplethysmogram (PPG) and the electrocardiogram (ECG). Given the diverse mechanisms by which the autonomic nervous system regulates BP, I developed methodologies using data from multiple individuals with BP perturbed by various, diverse, mechanisms. To identify surrogate measures of BP derived from the PPG and ECG signals, I designed a clinical study in which significant BP changes were induced through a pharmacological intervention in thirty healthy volunteers. Using data from this study, I established that changes in the pulse arrival time (PAT, the time delay between fiducial points on the ECG and PPG waveforms) and morphological features of the PPG waveform could provide reliable cuffless indicators for changes in BP. Even at rest, however, these signals are confounded by factors such as the pre-ejection period (PEP) and signal measurement noise. Additionally, accurate absolute measurements of BP required calibration using a reference BP device. Subsequently, I conducted a circadian analysis of these surrogate measures of BP using a large cohort of 1,508 patients during the 24-hour period prior to their discharge from an intensive care unit. Through this circadian analysis I suggest that PAT and a subset of features from the PPG waveform exhibit a phenotypically modified circadian rhythm in synchronicity with that of BP. Additionally, I designed a novel ordinal classification algorithm, which utilised circadian features of these signals, in order to identify BP circadian rhythm profiles in a calibration-free manner. This method may provide a cost-effective initial assessment of BP phenotypes in the general population. Notably, estimating absolute BP values using PPG and ECG signals in the ICU resulted in clinically significant mean absolute errors of 9.26 (5.01) mmHg. Finally, I designed a clinical study to extend the work towards cuffless ambulatory BP estimation in a cohort of fifteen healthy volunteers. Hybrid calibration strategies (where model personalisation was handled by user demographics, commonly utilised by commercial cuffless devices) led to clinically significant errors when estimating absolute values of BP, mean absolute error = 9.62 (19.73) mmHg. For the majority of individuals, a more appropriate estimation of BP values was achieved through an individual calibration strategy whereby idiosyncratic models were trained on personalised data, mean absolute error = 5.45 (6.40) mmHg. However, for a handful of individuals, notable estimation errors (>10 mmHg) still persisted using this strategy largely as a result of motion artifacts, inherent intra- and inter-individual variability in PPG features, and inadequate training data. Overall, I suggest that while beat-by-beat measurements of BP can be obtained using PPG and ECG signals, their accuracy is significantly limited in an ambulatory environment. This limitation, combined with the impracticality of individual calibration (due to the low tolerance for ABPM), suggest that cuffless ambulatory blood pressure measurement using the PPG and ECG signals may be infeasible. Nevertheless, macro assessments of cardiovascular health, such as an individual's BP phenotype, may be comparatively more accurately predicted using these signals with the potential to be recorded without calibration. Through further research on the relationship between the circadian rhythms of BP and the PPG and ECG waveforms, it is promising that these signals may be able to assist in detecting deterioration in cardiovascular health in the general population

Estimating pulse wave velocity using mobile phone sensors

Pulse wave velocity has been recognised as an important physiological phenomenon in the human body, and its measurement can aid in the diagnosis and treatment of chronic diseases. It is the gold standard for arterial stiffness measurements, and it also shares a positive relationship with blood pressure and heart rate. There exist several methods and devices via which it can be measured. However, commercially available devices are more geared towards working health professionals and hospital settings, requiring a significant monetary investment and specialised training to operate correctly. Furthermore, most of these devices are not portable and thus generally not feasible for private home use by the common individual. Given its usefulness as an indicator of certain physiological functions, it is expected that having a more portable, affordable, and simple to use solution would present many benefits to both end users and healthcare professionals alike. This study investigated and developed a working model for a new approach to pulse wave velocity measurement, based on existing methods, but making use of novel equipment. The proposed approach made use of a mobile phone video camera and audio input in conjunction with a Doppler ultrasound probe. The underlying principle is that of a two-point measurement system utilising photoplethysmography and electrocardiogram signals, an existing method commonly found in many studies. Data was collected using the mobile phone sensors and processed and analysed on a computer. A custom program was developed in MATLAB that computed pulse wave velocity given the audio and video signals and a measurement of the distance between the two data acquisition sites. Results were compared to the findings of previous studies in the field, and showed similar trends. As the power of mobile smartphones grows, there exists potential for the work and methods presented here to be fully developed into a standalone mobile application, which would bring forth real benefits of portability and cost-effectiveness to the prospective user base