[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125091-en":3,"doc-seo-125091-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125091,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Characterization of Potential Photoplethysmogram Blood Pressure Biomarkers - An Undergraduate Research Scholars Thesis - Machine Learning and Feature Selection","Cardiovascular disease remains a leading cause of death worldwide, and preventive continuous monitoring can reduce mortality and health-care burden. Photoplethysmography (PPG) is a noninvasive optical method that tracks blood-volume changes, enabling waveform-based biomarkers such as blood pressure, respiratory information, and heart-rate variability. Building on prior multimodal wearable research aimed at cuffless monitoring, this thesis addresses limited model generalizability by training multiple machine-learning models on different datasets and comparing selected features. Three model setups are evaluated using pyPPG-derived PPG waveform features with MRMR-based selection to identify more transferable biomarkers.","CHARACTERIZATION OF POTENTIAL PHOTOPLETHYSMOGRAM  \nBLOOD PRESSURE BIOMARKERS FROM SHORT-RECORDED  \nWAVEFORMS USING MACHINE-LEARNING AND FEATURE  \nSELECTION  \nAn Undergraduate Research Scholars Thesis  \nby  \nBENJAMIN DUNNING  \nSubmitted to the LAUNCH: Undergraduate Research office at Texas A&M University  \nin partial fulfillment of requirements for the designation as an  \nUNDERGRADUATE RESEARCH SCHOLAR  \nApproved by  \nFaculty Research Advisor: Dr. Gerard L. Cote  \nMay 2024  \nMajor: Computer Engineering  \nCopyright © 2024 . Benjamin Dunning.  \nRESEARCH COMPLIANCE CERTIFICATION  \nResearch activities involving the use of human subjects, vertebrate animals, and/or biohazards must be reviewed and approved by the appropriate Texas A&M University regulatory research committee (i.e., IRB, IACUC, IBC) before the activity can commence. This requirement applies to activities conducted at Texas A&M and to activities conducted at non-Texas A&M facilities or institutions. In both cases, students are responsible for working with the relevant Texas A&M research compliance program to ensure and document that all Texas A&M compliance obligations are met before the study begins.  \nI, Benjamin Dunning, certify that all research compliance requirements related to this Undergraduate Research Scholars thesis have been addressed with my Faculty Research Advisor prior to the collection of any data used in this final thesis submission.  \nThis project did not require approval from the Texas A&M University Research Compliance & Biosafety office.  \nTABLE OF CONTENTS  \nPage  \nABSTRACT.................................................................................................................................... 1  \nACKNOWLEDGEMENTS ............................................................................................................ 3  \n1. INTRODUCTION .................................................................................................................... 4  \n2. METHODS ............................................................................................................................... 8  \n2.1 Data Choice .................................................................................................................. 8  \n2.2 Data Preprocessing ..................................................................................................... 10  \n2.3 Feature Extraction....................................................................................................... 12  \n2.4 Feature Selection and Model Training ....................................................................... 14  \n2.5 Feature and Model Tuning ......................................................................................... 16  \n3. RESULTS ............................................................................................................................... 18  \n3.1 Machine Learning Results .......................................................................................... 18  \n3.2 Comparison of Features .............................................................................................. 19  \n4. CONCLUSION ....................................................................................................................... 21  \n4.1 Summary..................................................................................................................... 21  \n4.2 Discussion................................................................................................................... 23  \nREFERENCES ............................................................................................................................. 28  \nABSTRACT  \nCharacterization of Potential Photoplethysmogram Blood Pressure Biomarkers from ShortRecorded Waveforms Using Machine-Learning and Feature Selection  \nBenjamin Dunning  \nDepartment of Computer Engineering  \nTexas A&M University  \nFaculty Research Advisor: Dr. Gerard L. Cote  \nD","cbCaiuIi2HOVPFtV","https://ap.wps.com/l/cbCaiuIi2HOVPFtV","pdf",512901,1,33,"English","en",105,"# Abstract\n# Acknowledgements\n# 1. Introduction\n# 2. Methods\n## 2.1 Data Choice\n## 2.2 Data Preprocessing\n## 2.3 Feature Extraction\n## 2.4 Feature Selection and Model Training\n## 2.5 Feature and Model Tuning\n# 3. Results\n## 3.1 Machine Learning Results\n## 3.2 Comparison of Features\n# 4. Conclusion\n## 4.1 Summary\n## 4.2 Discussion\n# References","[{\"question\":\"How are PPG features selected for identifying potential blood-pressure biomarkers?\",\"answer\":\"PPG waveform features are extracted using the pyPPG Python library. The most important ten percent of features from each dataset are selected using the MRMR (minimum redundancy maximum relevancy) algorithm before model comparison.\"}]","Characterization of Potential Photoplethysmogram Blood Pressure Biomarkers - An Undergraduate Research Scholars Thesis - Machine Learning and Feature Selection | PDF",1785896580,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"characterization-of-potential-photoplethysmogram-blood-pressure-biomarkers-an-undergraduate-research-scholars-thesis-machine-learning-and-feature-selection","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/characterization-of-potential-photoplethysmogram-blood-pressure-biomarkers-an-undergraduate-research-scholars-thesis-machine-learning-and-feature-selection/125091/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How are PPG features selected for identifying potential blood-pressure biomarkers?","Question",{"text":75,"@type":76},"PPG waveform features are extracted using the pyPPG Python library. 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