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This study evaluates whether machine learning (LightGBM) applied to non-invasive photoplethysmography (PPG) waveform features can identify patients with elevated CVP. Features are extracted per cardiac cycle and aggregated per patient from a perioperative dataset (MLORD, UCLA). Patients are labeled using average maximum CVP as normal (5–15 mmHg) or elevated (>15 mmHg), yielding performance around AUC 0.79 and accuracy 0.71, supporting discriminative potential.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-identification-of-patients-with-elevated-central-venous-pressure-using-features-extracted-from-photoplethysmography-waveforms/440564/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-based-identification-of-patients-with-elevated-central-venous-pressure-using-features-extracted-from-photoplethysmography-waveforms/440564.png","ImageObject",300,407,{"name":92,"@type":93},"hdkawk","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address about CVP measurement?","Question",{"text":112,"@type":113},"CVP is typically measured invasively using central venous catheterization, which is time-consuming and carries risks such as vascular injury. The study seeks a non-invasive alternative using machine learning on PPG waveforms.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are patients labeled as normal or elevated CVP?",{"text":117,"@type":113},"The study uses the average of the maximum CVP values as ground truth. Patients are classified as normal when 5 ≤ CVP ≤ 15 mmHg and as elevated when CVP > 15 mmHg.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning model and features are used?",{"text":121,"@type":113},"A LightGBM model is trained using 843 PPG features per cardiac cycle, then aggregated per patient, alongside statistics derived from those PPG features and corresponding CVP maxima. Recursive feature elimination with cross-validation selects a subset for development.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},440564,1790813118,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},3985747859855,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","medRxiv preprint doi: [https://doi.org/10.64898/2025.12.30.25343231](https://doi.org/10.64898/2025.12.30.25343231); this version posted December 31, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY-ND 4.0 International license .  \nMachine Learning-Based Identification of Patients with Elevated Central Venous Pressure Using Features Extracted from Photoplethysmography Waveforms  \nRavi Pal 1, Akos Rudas2, Jeffrey N. Chiang2, Anna Barney3, and Maxime Cannesson1,*  \n1Department of Anesthesiology & Perioperative Medicine, University of California, Los Angeles, CA, USA.  \n2Department of Computational Medicine, University of California, Los Angeles, CA, USA.  \n3Institute of Sound and Vibration Research (ISVR), University of Southampton, Southampton, United Kingdom.  \n*[mcannesson@mednet.ucla.edu](mcannesson@mednet.ucla.edu)  \nABSTRACT  \nCentral venous pressure (CVP), a key component of hemodynamic monitoring, is widely used to guide fluid resuscitation in critically ill patients. It is typically measured using central venous line catheterization, which is the gold standard, but this method is invasive, timeconsuming, and associated with complications. This study aims to investigate whether machine learning (ML)-based analysis of features extracted from a non-invasive, standard-of-care waveform—the photoplethysmography (PPG) signal—can identify patients with elevated CVP. We trained Light Gradient-Boosting Machine (LightGBM) model using a large perioperative dataset (MLORD), containing 17,327 surgical patients from 2019 to 2022 at UCLA. For this study, we selected 1665 patients with both PPG and CVP waveforms available. A total of 843 PPG features per cardiac cycle (CC) were extracted from the PPG waveforms using a signal processing-based feature extraction tool, along with the simultaneous maximum value calculated from the corresponding CCs in the CVP waveform. Additionally, for each patient, the average and standard deviation of each PPG feature, as well as the mean of the maximum CVP values, were calculated across all cardiac cycles, resulting in 843 averaged PPG features, 843 PPG feature standard deviations, and one averaged maximum CVP value per patient. The average maximum CVP value was used as the ground truth to classify patients as either normal (5 ≤ CVP ≤ 15 mmHg) or elevated (CVP > 15 mmHg) . Of the 1,665 patients, 1,182 were normal and 483 were elevated. The dataset was split into 90% for training (1,063 normal and 435 elevated) and 10% for testing (119 normal and 48 elevated) . From the 1686 PPG features (843 averaged and 843 standard deviation), 246 were selected for model development using the Recursive Feature Elimination with Cross-Validation (RFECV) approach. To further enhance performance, hyperparameters were tuned through 5-fold cross-validation on the training set. Finally, the best-performing configuration was retrained on the full training data, and its performance was evaluated on the held-out test set. To provide a robust estimate and confidence interval, a bootstrapping procedure with 100 iterations was performed on the test set. The LightGBMclassifier achieved a mean area under the receiver operating characteristic curve (AUC) of 0.79 (95% CI: 0.71–0.84) and mean accuracy of 0.71 (95% CI: 0.65-0.77), demonstrating good discriminatory power in distinguishing between patients with normal and elevated CVP. This study highlights the ability of PPG-derived features to discriminate between patients with normal and elevated CVP using ML. These early findings lay the groundwork for future research aimed at developing non-invasive approaches to CVP assessment.  \nIntroduction  \nHuman biosignals such as photoplethysmography (PPG) and central venous pressure (CVP) waveforms carry important physiological information crucial for diagnosing and monitoring ","cbCaikkG5Zkk1hd6","https://ap.wps.com/l/cbCaikkG5Zkk1hd6","pdf",1487207,"English","# Abstract\n# Introduction\n## CVP measurement in clinical practice\n## Non-invasive PPG signals and ML rationale","[{\"question\":\"What problem does the study address about CVP measurement?\",\"answer\":\"CVP is typically measured invasively using central venous catheterization, which is time-consuming and carries risks such as vascular injury. The study seeks a non-invasive alternative using machine learning on PPG waveforms.\"},{\"question\":\"How are patients labeled as normal or elevated CVP?\",\"answer\":\"The study uses the average of the maximum CVP values as ground truth. Patients are classified as normal when 5 ≤ CVP ≤ 15 mmHg and as elevated when CVP \\u003e 15 mmHg.\"},{\"question\":\"Which machine learning model and features are used?\",\"answer\":\"A LightGBM model is trained using 843 PPG features per cardiac cycle, then aggregated per patient, alongside statistics derived from those PPG features and corresponding CVP maxima. Recursive feature elimination with cross-validation selects a subset for development.\"}]","Machine Learning-Based Identification of Patients with Elevated Central Venous Pressure Using Features Extracted from Photoplethysmography Waveforms | PDF",1790692535,23]