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 anomalies in a PPG waveform as they disrupt blood flow. We propose an automatic method for recognising these anomalies in PPG signal alone, without the need for ECG. We train an LSTM deep neural network on 400,000 clean PPG samples to learn typical PPG morphology and rhythm, and flag PPG signal diverging from this as cardiac abnormalities. We compare the cardiac abnormalities our approach recognises with the ectopic beats recorded by a bedside ECG monitor for 29 patients over 47.6 hours of gold standard observations. Our proposed cardiac abnormality recognition approach recognises 60%+ of ECG-detected PVCs in PPG signal, with a false positive rate of 23% - demonstrating the compelling power and value of this novel approach. Finally we examine how cardiac abnormalities manifest in PPG signal for in- and out-of-hospital patient populations using a wearable device during standard care.
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…
Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion Artifact (MA) reduction techniques have been developed and evaluated using either synthetic noisy signals or signals collected during high-intensity activities - both of which are difficult to generalize for real-life scenarios. Therefore, it is valuable to collect realistic PPG s…
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals
Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）；Nell Hodgson Woodruff School of Nursing, Emory University（埃默里大学内尔·霍奇森·伍德拉夫护理学院）；University of California San Francisco（加利福尼亚大学旧金山分校）
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
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）
In this work, we present the Senbiosys blood pressure monitoring algorithm (SB-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP).
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Pennsylvania State University（宾夕法尼亚州立大学）；United International University（联合国际大学）；University of Rochester（罗切斯特大学）；United Arab Emirates University（阿联酋大学）
Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a…
Remote Photoplethysmography (rPPG) is a fast-growing technique of vital sign estimation by analyzing video of a person. Several major phenomena affecting rPPG signals have been studied (e.g. video compression, distance from person to camera, skin tone, head motions). However, to develop a highly accurate rPPG method, new, minor, factors should be investigated. First considered factor is irregular frame rate of video recordings. Despite of PPG signal transformation by frame rate irregularity, no significant distortion of PPG signal spectra was found in the experiments. Second factor is rolling …
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）
Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
University of Connecticut（康涅狄格大学）；Morehouse School of Medicine（莫尔豪斯医学院）；The Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）
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）
Technion-Israel Institute of Technology（以色列理工学院）；University of Cambridge（剑桥大学）；University of London（伦敦大学）；Mines ParisTech - PSL University（巴黎国立高等矿业学校-巴黎文理研究大学）
Introduction: Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. It is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL). Methods: We used two publicly available sleep databases that included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We developed SleepPPG-Net, a DL model for 4-class sleep staging from the r…
Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings. A PPG dataset that is created for a particular use case is often imbalanced, due to a low prevalence of the pathological condition it targets to predict and the paroxysmal nature of the condition as well. To tackle this problem, we propose log-spectral matching GAN (LSM-GAN), a generative model that can be used as a data augmentation technique to alleviate the class …
SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography
The Faculty of Data and Decision Sciences, Technion, Israel Institute of Technology（以色列理工学院数据与决策科学学院）；Faculty of Biomedical Engineering, Technion, Israel Institute of Technology（以色列理工学院生物医学工程学院）
This letter presents a novel hybrid method that leverages deep learning to exploit the multi-resolution analysis capability of the wavelets, in order to denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy PPG sequence of length N is first decomposed into L detailed coefficients using the fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructed as follows. A custom feedforward neural network (FFNN) provides the binary weights for each of the wavelet sub-signals outputted by the inverse-FWT block. This way, all those sub-signals which correspond to …
AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能全国重点实验室）；National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）
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…
ThinGenious PC（ThinGenious个人计算机公司）；University of West Attica（西阿提卡大学）；University of Bologna（博洛尼亚大学）
Nowadays, Hearth Rate (HR) monitoring is a key feature of almost all wrist-worn devices exploiting photoplethysmography (PPG) sensors. However, arm movements affect the performance of PPG-based HR tracking. This issue is usually addressed by fusing the PPG signal with data produced by inertial measurement units. Thus, deep learning algorithms have been proposed, but they are considered too complex to deploy on wearable devices and lack the explainability of results. In this work, we present a new deep learning model, PULSE, which exploits temporal convolutions and multi-head cross-attention to…
Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers
Pázmány Péter Catholic University Faculty of Information Technology（帕兹曼尼·彼得天主教大学信息技术学院）；Faculty of Biomedical Engineering, Technion Institute of Technology（以色列理工学院生物医学工程学院）；Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW（新南威尔士大学悉尼大脑中心英厄姆应用医学研究所）
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…
In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart rate from wrist-type photoplethysmography (PPG) signals and simultaneously recorded three-axis acceleration data. HeartBEAT contains three major parts: spectrum estimation of PPG signals and acceleration data, elimination of motion artifacts in PPG signals using recursive least Square (RLS) adaptive filters, and auxiliary heuristics. We tested HeartBEAT on the 22 datasets provided in the 2015 IEEE Signal Processing Cup. The first ten datasets were recorded from subjects performing forearm and upper-arm exercises, jum…
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…
University of California Irvine（加州大学欧文分校）；University of California, Irvine（加州大学欧文分校）；Institute for Future Health, University of California, Irvine（加州大学欧文分校未来健康研究所）
Respiratory rate (RR) is a clinical sign representing ventilation. An abnormal change in RR is often the first sign of health deterioration as the body attempts to maintain oxygen delivery to its tissues. There has been a growing interest in remotely monitoring of RR in everyday settings which has made photoplethysmography (PPG) monitoring wearable devices an attractive choice. PPG signals are useful sources for RR extraction due to the presence of respiration-induced modulations in them. The existing PPG-based RR estimation methods mainly rely on hand-crafted rules and manual parameters tunin…
This work proposes a smartphone video-based approach for the estimation of blood glucose in a non-invasive way. Videos using smartphone camera are collected from the tip of the subjects finger and the frames are subsequently converted into Photoplethysmography (PPG) waveform. Gaussian filter along with Asymmetric Least Square methods have been applied on the PPG signals to remove the high-frequency noise, optical and motion interferences. Different signal features such as Systolic and Diastolic Peaks, the time difference between consecutive peaks (DelT), First Derivative peaks, and Second deri…
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；United International University（联合国际大学）；University of Maryland（马里兰大学）；United Arab Emirates University（阿联酋大学）
A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography
School of Computer Science and Engineering（计算机科学与工程学院）；Changchun University of Technology（长春理工大学）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）；Jilin University（吉林大学）；Smart Health Joint Innovation Laboratory for the New Generation of AI（新一代人工智能智能健康联合创新实验室）
PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection
National Institute of Health Data Science, Peking University, China（北京大学国家健康数据科学研究院）；School of Intelligence Science and Technology, Peking University, China（北京大学智能科学与技术学院）；Data Science Institute, Imperial College London, UK（伦敦帝国理工学院数据科学研究院）；University of Chinese Academy of Sciences, China（中国科学院大学）
University of Bologna（博洛尼亚大学）；Politecnico di Torino（都灵理工大学）；ETH Zurich（苏黎世联邦理工学院）
Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this problem, based on combining PPG signals with inertial sensor data. Until know, both commercial and reasearch solutions are computationally efficient but not very robust, or strongly dependent on hand-tuned parameters, which leads to poor generalizati…
