[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126923-en":3,"doc-seo-126923-105":30,"detail-sidebar-cat-0-en-105":91},{"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},126923,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","DIAGNOSTIC ASSISTANCE FOR THE PREDICTION OF CARDIAC DISEASES WITH A NON-INVASIVE ESTIMATION OF HEMODYNAMIC PARAMETERS - Thesis","Machine learning is applied to support diagnostic assistance for cardiac diseases through non-invasive estimation of hemodynamic parameters. The work frames cardiac output measurement theory alongside arterial blood pressure and cardiac cycle concepts, then details data exploration, feature engineering, and feature selection strategies. A supervised learning pipeline is built with cross-validation, including waveform processing and selection of algorithms. Results cover signal processing using WFDB, model performance comparisons, and interpretation through a discussion that leads to conclusions and future work directions.","DIAGNOSTIC ASSISTANCE FOR THE PREDICTION OF CARDIAC DISEASES WITH A NON-INVASIVE ESTIMATION OF HEMODYNAMIC PARAMETERS  \nUSING MACHINE LEARNING  \nBy  \nKamila Joseph Hernández Vanegas  \nA THESIS  \nSubmitted in partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nIn Electronics Engineering  \nUniversidad del Norte  \n2023  \n© Biomedical Signal Processing and Artificial Intelligence Laboratory (BSPAI)–  \nResearch Group  \nThis thesis has been approved in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE in In Electronics Engineering  \n.  \nDepartment of Electrical and Electronics Engineering  \nThesis Supervisor: Winston Spencer Percybrooks Bolivar, PhD.  \nProgram Coordinator: Mauricio Pardo Gonzales, PhD.  \nContenido  \n1 Background ...................................................................................................... 1  \n1.1 Problem Statement ..............................................................................5  \n2 Research Objectives .........................................................................................9  \n2.1 Aim .....................................................................................................9  \n2.2 Objectives ...........................................................................................9  \n2.3 Scope and Limitation ..........................................................................9  \n3 Theoretical Framework ..................................................................................11  \n3.1 Cardiac Output Measurement Theory...............................................11  \n3.2 Arterial Blood Pressure .....................................................................13  \n3.3 Cardiac Cycle .................................................................................... 14  \n3.4 Exploration of Data ........................................................................... 15  \n3.5 Feature Engineering .......................................................................... 15  \n3.6 Feature Selection Techniques ...........................................................15  \n3.7 Supervised Learning Algorithms ......................................................16  \n3.8 Cross-Validation ............................................................................... 17  \n4 Related Work.................................................................................................. 19  \n5 Proposed Approach ........................................................................................25  \n6 Research Methodology ...................................................................................28  \n6.1 General Description ..........................................................................28  \n6.2 Data Acquisition ...............................................................................29  \n6.3 Data Filtering ....................................................................................33  \n6.4 Waveform Processing Processing (MGH/MF) .................................36  \n6.5 Feature Extraction and Algorithm Selection.....................................39  \n7 Results ............................................................................................................47  \n7.1 Signal Processing with WFDB .........................................................47  \n7.2 Machine Learning algorithms ...........................................................51  \n8 Discussion and Conclusion ............................................................................55  \n8.1 Future Work ......................................................................................56  \n9 References ......................................................................................................57  \nList of Figures  \nFig.1 . 1. Characterization of channels with its corresponding signals, from the MIMIC II  \nwaveform visualizer.......................................","cbCaimE3LRedBGIf","https://ap.wps.com/l/cbCaimE3LRedBGIf","pdf",3146146,1,75,"English","en",105,"# Background\n## Problem Statement\n# Research Objectives\n## Aim\n## Objectives\n## Scope and Limitation\n# Theoretical Framework\n## Cardiac Output Measurement Theory\n## Arterial Blood Pressure\n## Cardiac Cycle\n## Exploration of Data\n## Feature Engineering\n## Feature Selection Techniques\n## Supervised Learning Algorithms\n## Cross-Validation\n# Related Work\n# Proposed Approach\n# Research Methodology\n## General Description\n## Data Acquisition\n## Data Filtering\n## Waveform Processing Processing (MGH/MF)\n## Feature Extraction and Algorithm Selection\n# Results\n## Signal Processing with WFDB\n## Machine Learning algorithms\n# Discussion and Conclusion\n## Future Work\n# References","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To provide diagnostic assistance for predicting cardiac diseases by estimating hemodynamic parameters using non-invasive data and machine learning.\"},{\"question\":\"Which data processing and modeling steps are included?\",\"answer\":\"The methodology covers data acquisition and filtering, waveform processing (MGH/MF), feature extraction and algorithm selection, and cross-validation for supervised learning.\"},{\"question\":\"How are the results evaluated and discussed?\",\"answer\":\"Results include signal processing with WFDB and performance of machine learning algorithms, followed by a discussion that concludes the work and outlines future directions.\"}]","DIAGNOSTIC ASSISTANCE FOR THE PREDICTION OF CARDIAC DISEASES WITH A NON-INVASIVE ESTIMATION OF HEMODYNAMIC PARAMETERS - Thesis | PDF",1785935679,189,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"diagnostic-assistance-for-the-prediction-of-cardiac-diseases-with-a-non-invasive-estimation-of-hemodynamic-parameters-thesis","",{"@graph":36,"@context":85},[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/diagnostic-assistance-for-the-prediction-of-cardiac-diseases-with-a-non-invasive-estimation-of-hemodynamic-parameters-thesis/126923/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To provide diagnostic assistance for predicting cardiac diseases by estimating hemodynamic parameters using non-invasive data and machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data processing and modeling steps are included?",{"text":80,"@type":76},"The methodology covers data acquisition and filtering, waveform processing (MGH/MF), feature extraction and algorithm selection, and cross-validation for supervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the results evaluated and discussed?",{"text":84,"@type":76},"Results include signal processing with WFDB and performance of machine learning algorithms, followed by a discussion that concludes the work and outlines future directions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]