This study explores extended feature space that is indicative of blood pressure (BP) changes for better estimation of continuous BP in an unobtrusive way. A total of 222 features were extracted from noninvasively acquired electrocardiogram (ECG) and photoplethysmogram (PPG) signals with the subject undergoing coronary angiography and/or percutaneous coronary intervention, during which intra-arterial BP was recorded simultaneously with the subject at rest and while administering drugs to induce BP variations. The association between the extracted features and the BP components, i.e. systolic BP (SBP), diastolic BP (DBP), mean BP (MBP), and pulse pressure (PP) were analyzed and evaluated in terms of correlation coefficient, cross sample entropy, and mutual information, respectively. Results show that the most relevant indicator for both SBP and MBP is the pulse full width at half maximum, and for DBP and PP, the amplitude between the peak of the first derivative of PPG (dPPG) to the valley of the second derivative of PPG (sdPPG) and the time interval between the peak of R wave and the sdPPG, respectively. As potential inputs to either the knowledge-guided model or data-driven method for cuffless BP calibration, the proposed expanded features are expected to improve the estimation accuracy of cuffless BP.
Goal: Although photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due to various diseases of circulatory system and interaction of other physiological systems make the extraction of such features very difficult. Methods: In this work, we propose a waveform-based hierarchical Artificial Neural Network - Long Short Term Memory (ANN-LSTM) model for BP estimation. The model consists of two hierarchy levels, where the lower hierarchy level uses ANNs…
Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Polytechnic University（香港理工大学）；Shenzhen Polytechnic University（深圳职业技术大学）
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）
The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices. Continuous PPG signal preprocessed and used as input of the Artificial Neural Network (ANN), and outputs systolic BP (SBP), diastolic BP (DBP), and mean arterial BP (MAP) values for each heartbeat. The improvement of the BPM accuracy is obtained by removing outliers in the preprocessing step and the whole-based inputs compared to parameter-based inputs extracted from the PPG signal. Performance obtaine…
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…
Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization
i2Cat Foundation（i2Cat基金会）；NEC Laboratories Europe GmbH（欧洲NEC实验室）；ICREA（加泰罗尼亚研究与高级研究所）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；TU Berlin（柏林工业大学）；University of Antwerp（安特卫普大学）
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；National Taiwan University（国立台湾大学）；Center for Geriatrics and Gerontology, Taipei Veterans General Hospital（台北荣民总医院老年医学与老年学中心）；Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University（国立阳明交通大学健康长寿与老化科学中心）；Kai-Yan Clinic（凯燕诊所）；School of Medicine, National Yang Ming Chiao Tung University（国立阳明交通大学医学院）；Institute of Clinical Medicine, National Yang Ming Chiao Tung University（国立阳明交通大学临床医学研究所）；Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital（台北荣民总医院内科部心脏科）；Cardiovascular Research Center, Taipei Veterans General Hospital（台北荣民总医院心血管研究中心）
