[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128273-en":3,"doc-seo-128273-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128273,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Machine Learning Approaches for Heart Rate Variability Data Correction and Coronary Artery Calcification Classification","Coronary artery disease (CAD) is a leading cause of death worldwide, and coronary artery calcification (CAC) is commonly assessed with coronary computed tomography angiography (CCTA). Prior work shows exercise physiology differs between individuals with and without CAC. This thesis applies machine learning to exercise-measured data to predict CAC, emphasizing heart rate (HR) and heart rate variability (HRV) from chest straps. Signal artifacts in HR monitors are corrected using time-series methods and evaluated alongside hemodynamic measures collected during a 91-km mountain bike race. Classification results show autoregressive integrated moving average (ARIMA) outperforms cubic interpolation for HRV correction, and a combined dimensionality reduction with logistic regression model reaches 84% accuracy using blood pressure, age, HRV, and BMI.","Machine Learning Approaches for Heart Rate Variability Data Correction and Coronary Artery Calcification Classification  \nby  \nJakob Svane  \nThesis submitted in fulfillment of  \nthe requirements for the degree of  \nPHILOSOPHIAE DOCTOR  \n(PhD)  \nFaculty of Science and Technology Department of Electrical Engineering and Computer Science  \nUniversity of Stavanger N-4036 Stavanger NORWAY  \n[www](www.uis.no)[.](www.uis.no)[uis](www.uis.no)[.](www.uis.no)[no](www.uis.no)  \n© Jakob Svane, 2025 All rights reserved.  \nISBN 978-82-8439-350-6 ISSN 1890-1387  \nPhD Thesis UiS no. 845  \nPreface  \nThis thesis is submitted as a partial fulfillment of the requirements for the degree of Philosophiae Doctor at the University of Stavanger, Norway. The research was conducted at the Department of Electrical Engineering and Computer Science, University of Stavanger from September 2021 to September 2024 . The compulsory courses were taken at the University of Stavanger.  \nThis thesis is based on a collection of four peer-reviewed and published scientific papers. These papers are included as chapters.  \nThe author acknowledges the use of generative AI tools, including Gram[marly.com and Perplexity.ai](marly.com and Perplexity.ai), in this thesis. However, the content remains entirely the product of the author.  \nJakob Svane, March 2025  \niv  \nAbstract  \nCoronary artery disease (CAD) is one of the most common cardiovascular diseases and a major cause of death worldwide. Detection of coronary artery calcification (CAC) through coronary computed tomography angiography (CCTA) is normally used to diagnose CAD. Previous studies have demonstrated significant differences in the physiological response to exercise between individuals with and without CAC. This thesis aimed to apply machine learning (ML) methods on data measured during exercise to predict the presence of CAC, with a particular focus on the analysis of heart rate (HR) and heart rate variability (HRV) measured with HR chest straps. For this purpose, signal issues from the HR monitors must be handled appropriately.  \nHemodynamic measures were collected from healthy participants before, during, and after a 91-km mountain bike race (the North Sea Race) . The presence of CAC was determined by CCTA after the race. Several time series methods were applied to the HRV data to address data artifact correction. A statistical analysis of hemodynamic measures at the most challenging hill was conducted to determine physiological differences between individuals with and without CAC. Finally, various classification algorithms were used to predict the presence of CAC based on hemodynamic and HRV data.  \nIn Papers 1 and 2, the autoregressive integrated moving average method was shown to outperform other artifact correction methods for HRV data, even with minimal training data and computational cost. Cubic interpolation, the most common artifact correction method, was found to be less effective and is therefore not recommended.  \nPaper 3 demonstrated that during prolonged high-intensity endurance exercise, diastolic blood pressure and HRV were the most important predictors of the presence of CAC. The level of physiological strain seems tobe an essential factor in inducing these differences in otherwise healthy individuals.  \nIn Paper 4, an ML approach combining dimensionality reduction with logistic regression achieved 84% accuracy for classifying individuals with and without CAC. This model’s most important input features were blood pressure, age, HRV, and body mass index. Overall, the results suggest that feature-based statistical analysis of HR and HRV data is more effective than raw-signal analysis, likely due to the high volatility of the signal data.  \nAcknowledgements  \nI want to thank my supervisors, Professor Tomasz Wiktorski and Professor Stein Ørn, for their excellent supervision and support throughout my PhD. Thank you to my co-supervisor, Professor Trygve Eftestøl, for his continuous support and insigh","cbCainIfPJpodo7d","https://ap.wps.com/l/cbCainIfPJpodo7d","pdf",4792470,5,1,116,"English","en",105,"# Preface\n# Abstract\n## Aim and study approach\n## Data acquisition and artifact correction\n# Main findings by paper\n## Papers 1 and 2: HRV artifact correction\n## Paper 3: predictors during endurance exercise\n## Paper 4: classification performance and key features\n# Acknowledgements\n# List of Abbreviations","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To apply machine learning methods to exercise-measured HR and HRV data to predict the presence of coronary artery calcification (CAC).\"},{\"question\":\"How is CAC determined in the study?\",\"answer\":\"CAC presence is determined using coronary computed tomography angiography (CCTA) after the 91-km mountain bike race.\"},{\"question\":\"Which HRV artifact correction method performed best?\",\"answer\":\"The autoregressive integrated moving average (ARIMA) method outperformed other correction methods, including cubic interpolation, and is recommended especially with minimal training data and computation.\"}]","Machine Learning Approaches for Heart Rate Variability Data Correction and Coronary Artery Calcification Classification | 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is the main goal of the thesis?","Question",{"text":77,"@type":78},"To apply machine learning methods to exercise-measured HR and HRV data to predict the presence of coronary artery calcification (CAC).","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is CAC determined in the study?",{"text":82,"@type":78},"CAC presence is determined using coronary computed tomography angiography (CCTA) after the 91-km mountain bike race.",{"name":84,"@type":75,"acceptedAnswer":85},"Which HRV artifact correction method performed best?",{"text":86,"@type":78},"The autoregressive integrated moving average (ARIMA) method outperformed other correction methods, including cubic interpolation, and is recommended especially with minimal training data and 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