Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
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）
Milad Asgari Mehrabadi, Seyed Amir Hossein Aqajari, Amir Hosein Afandizadeh Zargari, Nikil Dutt, Amir M. Rahmani
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) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends theGAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2x in BP estimation.
VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling
Department of Electrical and Computer Engineering（电气与计算机工程系）；Clarkson University（克林顿大学）；Department of Computer Science（计算机科学系）；Worcester Polytechnic Institute（沃思格理工学院）
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…
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
AI4Health Department, University of Oldenburg（奥尔登堡大学AI4Health部门）；Department of Data Science and AI, National Physical Laboratory（国家物理实验室数据科学与人工智能部门）；School of Mathematics and Physics, University of Surrey（萨里大学数学与物理学院）
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…
PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation
Department of Computer Science, Emory University（计算机科学系，埃默里大学）；Nell Hodgson Woodruff School of Nursing, Emory University（Nell Hodgson Woodruff护理学院，埃默里大学）
EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography
Centre for Digital Health Interventions, ETH Zurich（数字健康干预中心，苏黎世联邦理工学院）；Centre for Digital Health Interventions, University of St.Gallen（数字健康干预中心，圣加伦大学）
University of Cambridge（剑桥大学）；Cantab Capital Institute for Mathematics of Information（Cantab Capital 数学信息研究所）；Department of Clinical Neurosciences（临床神经科学系）；Department of Medicine（医学系）；Division of Anaesthesia（麻醉科）；Department of Applied Mathematics and Theoretical Physics（应用数学与理论物理系）
Waveform physiological data is important in the treatment of critically ill patients in the intensive care unit. Such recordings are susceptible to artefacts, which must be removed before the data can be re-used for alerting or reprocessed for other clinical or research purposes. Accurate removal of artefacts reduces bias and uncertainty in clinical assessment, as well as the false positive rate of intensive care unit alarms, and is therefore a key component in providing optimal clinical care. In this work, we present DeepClean; a prototype self-supervised artefact detection system using a con…
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…
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…
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…
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…
University of Oulu（奥卢大学）；VTT Technical Research Centre of Finland Ltd（芬兰VTT技术研究中心有限公司）
Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extract…
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）
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西特齐大学）；Laboratoire national de métrologie et d’essais（国家计量与测试实验室）；Biomedical Engineering Institute, Kaunas University of Technology（克莱萨斯理工大学生物医学工程研究所）；Physikalisch-Technische Bundesanstalt（德国物理技术部）；National Physical Laboratory（国家物理实验室）；King’s College London（伦敦国王学院）；University of Surrey（萨里大学）；Department of Public Health and Primary Care, University of Cambridge（剑桥大学公共卫生与初级保健系）
Stress is a physiological state that hampers mental health and has serious consequences to physical health. Moreover, the COVID-19 pandemic has increased stress levels among people across the globe. Therefore, continuous monitoring and detection of stress are necessary. The recent advances in wearable devices have allowed the monitoring of several physiological signals related to stress. Among them, wrist-worn wearable devices like smartwatches are most popular due to their convenient usage. And the photoplethysmography (PPG) sensor is the most prevalent sensor in almost all consumer-grade wri…
Robust Photoplethysmography Signal Denoising via Mamba Networks
National Taiwan University（国立台湾大学）；Research Center for Information Technology Innovation, Academia Sinica（学术院资讯科技创新研究中心）；Graduate Institute of Communication Engineering, National Taiwan University（国立台湾大学通信工程研究所）；Department of Electrical Engineering, National Taiwan University（国立台湾大学电机工程系）
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"）
Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes
Taipei Veterans General Hospital（台北荣民总医院）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Duke University（杜克大学）
Leipzig University of Applied Sciences（莱比锡应用科学大学）；University Hospital Leipzig（莱比锡大学医院）
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 predictio…
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…
Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data
University of Toronto（多伦多大学）；SickKids（SickKids医院（多伦多儿童医院））；Vector Institute（向量研究所）；University of Sydney（悉尼大学）；University of Oxford（牛津大学）；Technion - Israel Institute of Technology（以色列理工学院）；Canadian Institute for Advanced Research（加拿大高级研究院）；Seattle Children’s Hospital（西雅图儿童医院）
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）
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）
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）
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）
SpO$_2$ Predictor-Guided Stage-Wise Time-Frequency Reconstruction of Low-Quality Dual-Wavelength PPG for Oxygen Saturation Estimation
Department of Computer Science, University of California, Davis, Davis, CA, U.S.A.（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, Davis, CA, U.S.A.（加州大学戴维斯分校电气与计算机工程系）
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）
