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 signals while performing Activities of Daily Living (ADL) to develop practical signal denoising and analysis methods. In this work, we propose an automatic pseudo clean PPG generation process for reliable PPG signal selection. For each noisy PPG segment, the corresponding pseudo clean PPG reduces the MAs and contains rich temporal details depicting cardiac features. Our experimental results show that 71% of the pseudo clean PPG collected from ADL can be considered as high quality segment where the derived MAE of heart rate and respiration rate are 1.46 BPM and 3.93 BrPM, respectively. Therefore, our proposed method can determine the reliability of the raw noisy PPG by considering quality of the corresponding pseudo clean PPG signal.
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）
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）
Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data
Taipei Veteran General Hospital（台北荣民总医院）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；University of Washington（华盛顿大学）；Duke University（杜克大学）；National Center for Theoretical Sciences（理论科学研究中心）
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）
Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition and lightweight CNN Architecture
Indian Institute of Engineering Science and Technology, Shibpur（希布尔印度工程科学与技术学院）；Hong Kong University of Science and Technology（香港科技大学）
光电体积描记（PPG）信号包括与心肺健康相关的生理信息。然而，在记录时，这些PPG信号容易被运动伪影和身体运动破坏，导致噪声富集、质量差的信号。因此，确保高质量的信号是必要的，以准确地提取心肺信息。虽然存在几种基于规则和基于机器学习（ML）的PPG信号质量估计方法，但这些算法的有效性值得怀疑。因此，这项工作提出了一个轻量级的CNN架构的信号质量评估采用了新的量子模式识别（QPR）技术。所提出的算法进行了验证手动注释的数据从昆士兰大学的数据库。总共28366.5s信号段被预处理并转换为20 x 500像素的图像文件。图像文件被视为2D CNN架构的输入。开发的模型将PPG信号分类为“好”或“坏”，准确度为98.3%，灵敏度为99.3%，特异性为94.5%，F1评分为98.9%。最后，所提出的框架的性能进行了验证，对嘈杂的“Welltory应用程序”收集PPG数据库。即使在嘈杂的环境中，所提出的架构证明了其能力。实验分析表明，一个苗条的架构，以及一种新的时空模式识别技术，提高了系统的性能。因此，所提出的方法可以用于对资源受限的可穿戴实施方式的好的和坏的PPG信号进行分类。
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）
