Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera-based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG-based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we apply this parameterization to a larger PPG dataset and train NNs to predict BP. The resulting prediction errors increase towards less frequent BP values. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply a personalization technique and retrain our NNs with subject-specific data. This slightly reduces the prediction errors.
Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis（加州大学戴维斯分校电气与计算机工程系）
Purdue University（普渡大学）；Qatar University（卡塔尔大学）；University of British Columbia（不列颠哥伦比亚大学）；BUET（孟加拉国工程技术大学）
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 m…
Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuously acquired, such as photoplethysmography (PPG), show some similarities with the Aortic Pressure waveform. Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal. Building on these results, we propose an advanced personalized…
Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis（加州大学戴维斯分校电气与计算机工程系）
Infant Research Centre（婴儿研究中心）；School of Computing（计算机学院）；Dublin City University（都柏林城市大学）；Department of Electrical Engineering（电气工程系）；KU Leuven（鲁汶大学）；Cork University Maternity Hospital（科克大学妇产医院）；Insight SFI Research Centre（Insight SFI研究中心）
Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is capable of inferring ABP from a single optical phot…
Qatar University（卡塔尔大学）；Universiti Kebangsaan Malaysia（马来西亚国立大学）；BUET（孟加拉国工程技术大学）
Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous Blood Pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in the hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP fr…
Benchmarking and Enhancing PPG-Based Cuffless Blood Pressure Estimation Methods
Department of Engineering Technology, University of Houston, Texas, USA（工程科技系，休斯顿大学，德克萨斯州，美国）；Department of Electrical and Computer Engineering, University of Houston, Texas, USA（电气与计算机工程系，休斯顿大学，德克萨斯州，美国）；Department of Information Science Technology, University of Houston, Texas, USA（信息科学科技系，休斯顿大学，德克萨斯州，美国）；Department of Cardiovascular Surgery, Houston Methodist Hospital, Texas, USA（心血管外科系，休斯顿方法医院，德克萨斯州，美国）；Department of Biomedical Engineering, University of Houston, Texas, USA（生物医学工程系，休斯顿大学，德克萨斯州，美国）
Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
VUNO Inc.（VUNO公司）；University of California, Irvine（加州大学欧文分校）；Research Institute for Future Medicine, Samsung Medical Center（三星未来医学研究所）；Inha University Hospital（仁荷大学医院）
Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a promising candidate for modern BP measurements, as PPG signals can be easily obtained from wearable devices in a non-invasive manner, allowing quick BP measurement. However, the performance of existing machine learning-based BP measuring methods still fall behind some BP measurement guidelines and most of them provide only point estimates of systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we present a cutting-edge method which is capable of continuously monitoring BP from the PPG signal a…
TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing
Faculty of Computer Science（计算机科学学院）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程学院）；University of New Brunswick（新不伦瑞克大学）；Concordia University（康考迪亚大学）
We propose an end-to-end framework to measure people's vital signs including Heart Rate (HR), Heart Rate Variability (HRV), Oxygen Saturation (SpO2) and Blood Pressure (BP) based on the rPPG methodology from the video of a user's face captured with a smartphone camera. We extract face landmarks with a deep learning-based neural network model in real-time. Multiple face patches also called Regions-of-Interest (RoIs) are extracted by using the predicted face landmarks. Several filters are applied to reduce the noise from the RoIs in the extracted cardiac signals called Blood Volume Pulse (BVP) s…
Leipzig University of Applied Sciences（莱比锡应用技术大学）；Leipzig University Medical Center（莱比锡大学医学中心）；University of Leipzig（莱比锡大学）
Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification methods. The latter assign PPG signals to predefined BP intervals that represent clinically relevant ranges. The former predict systolic (SBP) and diastolic (DBP) BP as continuous variables and are of particular interest to the research community. However, the reported accuracies of B…
SSN College of Engineering（SSN工程学院）；College of Engineering, Guindy（Guindy工程学院）；Solarillion Foundation（Solarillion基金会）
Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate…
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Rostock University Medical Center（罗斯托克大学医学中心）；Charité-Universitätsmedizin Berlin（柏林夏里特医学院）
This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as wel…
North South University（北南大学）；Qatar University（卡塔尔大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
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 st…
End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
Interuniversity Department of Regional and Urban Studies and Planning at Politecnico di Torino（理工学院区域与城市研究与规划联合大学部门）；Department of Control and Computer Engineering at Politecnico di Torino（理工学院控制与计算机工程系）；Department of Information Technology and Electrical Engineering at ETH Zurich（苏黎世联邦理工学院信息技术与电气工程系）；Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" at University of Bologna（博洛尼亚大学电子、电子与信息工程系"Guglielmo Marconi"）
University of California Irvine（加利福尼亚大学尔湾分校）；Department of Electrical Engineering and Computer Science, University of California Irvine（加利福尼亚大学尔湾分校电气工程与计算机科学系）；Department of Computer Science, University of California, Irvine（加利福尼亚大学尔湾分校计算机科学系）；Institute for Future Health, University of California, Irvine（加利福尼亚大学尔湾分校未来健康研究所）
Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP…
Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios
Faculty of Frontier Sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；University of Melbourne（墨尔本大学）；University of New South Wales（新南威尔士大学）
A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: i) high power consumption and ii) the need to int…
INN-PAR: Invertible Neural Network for PPG to ABP Reconstruction
Rekhi Centre of Excellence for the Science of Happiness（雷克希幸福科学卓越中心）；IIT Kharagpur（印度理工学院卡拉格普尔分校）；Department of EE（电气工程系）；Department of E&ECE（电气工程与电子通信工程系）
Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
AI4Health Department, Oldenburg University（AI4Health部门，旧恩斯特堡大学）；Department of Data Science and AI, National Physical Laboratory（数据科学与人工智能系，国家物理实验室）；School of Mathematics and Physics, University of Surrey（数学与物理学院，萨里大学）；Department of Public Health and Primary Care, University of Cambridge（公共卫生与初级保健系，剑桥大学）
Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）
The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed…
Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact
Singapore Management University（新加坡管理大学）；The Chinese University of Hong Kong（香港中文大学）；University of Cambridge（剑桥大学）；Eindhoven University of Technology（埃因霍温理工大学）
Concordia University（康考迪亚大学）；Case Western Reserve University（凯斯西储大学）
Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring. In this regard, a robust deep learning-based framework is proposed for computation of low latency, continuous, and calibration-free upper and lower bounds on the systolic and diastolic BP. Method: Referred to as the BP-Net, the proposed framework is a novel convolutional architecture that provides longer effective memory while achieving superior performance due to incorporation of casual dialated convolutions and residual connections. To utilize t…
Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show…
