The Electrocardiograph signal represents the heart's electrical activity while blood pressure results from the heart's mechanical activity. Previous studies have investigated how the heart's electrical and mechanical activities are related and have referred to their relationship as the Mechano-Electric Coupling term. A new method to estimate the blood pressure including is proposed which uses only the Electrocardiograph signal. In spite of studies performed on feature extraction based on the signals' physiological parameters (Parameter-based), in this work, the feature vectors are formed with samples of the Electrocardiograph signal in a particular time frame (Whole-based) and these vectors are input into Adaptive Boosting Regression to estimate blood pressure. The nonlinear relationship which correlates blood pressure with the Electrocardiograph signal is concluded by the results of this study. According to the results, the used algorithms, for estimating both diastolic blood pressures and mean arterial pressure, are in compliance with the standards of the Association for the Advancement of Medical Instrumentation. Also, according to the British Hypertension Society standard, estimating diastolic blood pressures and mean arterial pressure with the proposed method attain an A grade while it achieves B for systolic blood pressure. The results indicate that using the introduced method, blood pressure can be estimated continuously, noninvasively, without cuff, calibration-free and by using only the Electrocardiograph signal.
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…
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…
This paper presents a novel blood pressure (BP) estimation method based on pulse transit time (PTT) and pulse arrival time (PAT) to estimate the systolic BP (SBP) and the diastolic BP (DBP). A data acquisition hardware is designed for high-resolution sampling of phonocardiogram (PCG), photoplethysmogram, and electrocardiogram (ECG). PCG and ECG perform as the proximal timing reference to obtain PTT and PAT indices, respectively. In order to derive a BP estimator model, a calibration procedure, including a supervised physical exercise, is conducted for each individual, which causes changes in t…
Ingenuity Labs Research Institute（Ingenuity Labs 研究院）
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an advers…
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…
University of Moratuwa（莫拉图瓦大学）；Lady Ridgeway Hospital for Children（里奇韦夫人儿童医院）
Diagnosing pre-existing heart diseases early in life is important as it helps prevent complications such as pulmonary hypertension, heart rhythm problems, blood clots, heart failure and sudden cardiac arrest. To identify such diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey important information. Therefore, effectively using these two modalities of data has the potential to improve the disease screening process. We evaluate this hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which contains simultaneously acquired PCG and ECG recordings. Our novel Dua…
The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze the arrhythmia problem from the ECG signals because of its fewer difficulties and cheapness. The heart peaks shown in the ECG graph are used to detect heart diseases, and the R peak is used to analyze arrhythmia disease. Arrhythmia is grouped into two groups - Tachycardia and Brad…
Smartwatch Photoplethysmography-Derived Heart Age via ECG-Guided Cross-Modal Pretraining as a Digital Biomarker of Vascular Aging
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据科学国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医药技术学院）；OPPO Health Lab（OPPO健康实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学学院）；Xidian University（西安电子科技大学）；Shenzhen Technology University（深圳职业技术大学）
Cardiac abnormalities affecting heart rate and rhythm are commonly observed in both healthy and acutely unwell people. Although many of these are benign, they can sometimes indicate a serious health risk. ECG monitors are typically used to detect these events in electrical heart activity, however they are impractical for continuous long-term use. In contrast, current-generation wearables with optical photoplethysmography (PPG) have gained popularity with their low-cost, lack of wires and tiny size. Many cardiac abnormalities such as ectopic beats and AF can manifest as both obvious and subtle …
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）
Wrist photoplethysmography (PPG) allows unobtrusive monitoring of the heart rate (HR). PPG is affected by the capillary blood perfusion and the pumping function of the heart, which generally deteriorate with age and due to presence of cardiac arrhythmia. The performance of wrist PPG in monitoring beat-to-beat HR in older patients with arrhythmia has not been reported earlier. We monitored PPG from wrist in 18 patients recovering from surgery in the post anesthesia care unit, and evaluated the inter-beat interval (IBI) detection accuracy against ECG based R-to-R intervals (RRI). Nine subjects h…
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…
University of Birjand（比尔詹德大学）；Mazandaran University of Science and Technology（马赞达兰科技大学）；University of Qom（库姆大学）；University of Genova（热那亚大学）；Deakin University（迪肯大学）；Bu-Ali Sina University（布-阿里·西纳大学）；Maragheh Faculty of Medical Sciences（马拉盖医学院）；Obuda University（奥布达大学）；Singapore University of Social Sciences（新加坡社会科学大学）；National Heart Centre Singapore（新加坡国家心脏中心）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other bio…
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…
Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
Objective: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenarios, the use of additional physiological signals such as arterial blood pressure (BP) has recently been suggested. There, estimation of heartbeat location requires information fusion from multiple signals. However, reported efforts in this direction often obtain multimodal estimates somewhat indirectly, by voting among separately obtained signal-spe…
With the ongoing heart problems of the population worldwide, the medical requirements of the people are expected to increase. Electrocardiogram (ECG) is one of the proven to capture the heart response signal to assess the electrical and muscular functions of the heart. The ECG setup is expensive and needs proper training, and of course, it is not instant. For fast, accurate heart parameter monitoring, scientists pay attention to the photoplethysmogram signal (PPG), based on the light intensity of a particular wavelength. Android smartphone with a good quality camera has come to ordinary people…
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）
