[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122773-en":3,"doc-seo-122773-105":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},122773,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning for the Classification of Atrial Fibrillation utilizing Seismo-and Gyrocardiogram","Cardiovascular diseases account for a substantial share of global mortality, driving growing demand for higher-quality healthcare and effective remote monitoring. Atrial fibrillation requires prolonged heart activity assessment for accurate diagnosis and severity evaluation, yet ECG Holter monitoring may not always be available. This doctoral thesis studies how to classify atrial fibrillation using short seismo- and gyrocardiogram recordings captured by smartphones. It evaluates supervised machine learning pipelines for clinical and patient-collected measurements, extends analysis to acute decompensated heart failure, and investigates deep neural networks for end-to-end feature learning and classification.","MACHINE LEARNING  \nFOR THE CLASSIFICATION OF ATRIAL FIBRILLATION UTILIZING SEISMO-AND GYROCARDIOGRAM  \nSaeed Mehrang  \nTURUN YLIOPISTON JULKAISUJA – ANNALES UNIVERSITATIS TURKUENSIS SARJA – SER. F OSA – TOM. 30 | TECHNICA – INFORMATICA | TURKU 2023  \nMACHINE LEARNING FOR THE CLASSIFICATION OF ATRIAL FIBRILLATION UTILIZING SEISMO-AND GYROCARDIOGRAM  \nSaeed Mehrang  \nTURUN YLIOPISTON JULKAISUJA – ANNALES UNIVERSITATIS TURKUENSIS SARJA – SER. F OSA – TOM. 30 | TECHNICA – INFORMATICA| TURKU 2023  \nUniversity of Turku  \nFaculty of Technology  \nDepartment of Computing  \nInformation and Communication Technology Doctoral Programme in Technology (DPT)  \nSupervised by  \n\n| Prof. Pasi Liljeberg University of Turku\u003Cbr>Adjunct. Prof. Mikko Pnkl University of Turku\u003Cbr>Reviewed by |\n| --- |\n| Prof. Omer Inan\u003Cbr>Georgia Institute of Technology\u003Cbr>Opponent |\n\nAssoc. Prof. Samuel Emil Schmidt Aalborg University  \nAssoc. Prof. Antti Airola University of Turku  \nAssoc. Prof. Kouhyar Tavakolian University of North Dakota  \nThe originality of this publication has been checked in accordance with the University of Turku quality assurance system using the Turnitin OriginalityCheck service.  \nISBN 978-951-29-9542-4 (PRINT)  \nISBN 978-951-29-9543-1 (PDF)  \nISSN 2736-9390 (PRINT)  \nISSN 2736-9684 (ONLINE)  \nPainosalama, Turku, Finland 2023  \nI dedicate this thesis to my late father and grandfather  \nShahram and Yosuf Mehrang  \nUNIVERSITY OF TURKU Faculty of Technology Department of Computing  \nInformation and Communication Technology  \nMEHRANG, SAEED: Machine Learning for the Classification of Atrial Fibrillation utilizing Seismo-and Gyrocardiogram  \nDoctoral dissertation, 138 pp.  \nDoctoral Programme in Technology (DPT) December 2023  \nABSTRACT  \nA significant number of deaths worldwide are attributed to cardiovascular diseases (CVDs), accounting for approximately one-third of the total mortality in 2019, with an estimated 18 million deaths. The prevalence of CVDs has risen due to the increasing elderly population and improved life expectancy. Consequently, there isan escalating demand for higher-quality healthcare services. Technological advancements, particularly the use of wearable devices for remote patient monitoring, have significantly improved the diagnosis, treatment, and monitoring of CVDs.  \nAtrial fibrillation (AFib), an arrhythmia associated with severe complications and potential fatality, necessitates prolonged monitoring of heart activity for accurate diagnosis and severity assessment. Remote heart monitoring, facilitated by ECG Holter monitors, has become a popular approach in many cardiology clinics. However, in the absence of an ECG Holter monitor, other remote and widely available technologies can prove valuable. The seismo-and gyrocardiogram signals (SCG and GCG) provide information about the mechanical function of the heart, enabling AFib monitoring within or outside clinical settings. SCG and GCG signals can be conveniently recorded using smartphones, which are affordable and ubiquitous in most countries.  \nThis doctoral thesis investigates the utilization of signal processing, feature engineering, and supervised machine learning techniques to classify AFib using short SCG and GCG measurements captured by smartphones. Multiple machine learning pipelines are examined, each designed to address specific objectives. The first objective (O1) involves evaluating the performance of supervised machine learning classifiers in detecting AFib using measurements conducted by physicians in a clinical setting. The second objective (O2) is similar to O1, but this time utilizing measurements taken by patients themselves. The third objective (03) explores the performance of machine learning classifiers in detecting acute decompensated heart failure (ADHF) using the same measurements as O1, which were primarily collected for AFib detection. Lastly, the fourth objective (O4) delves into the application of deep neural networks for automated feature le","cbCaiqRanXwmi2wA","https://ap.wps.com/l/cbCaiqRanXwmi2wA","pdf",1377626,1,86,"English","en",105,"# Abstract\n## Background and clinical motivation\n## Data sources and signal modalities\n## Research objectives and methods\n## Results and key findings\n## Limitations and clinical positioning","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To classify atrial fibrillation using short seismo- and gyrocardiogram measurements recorded by smartphones, applying signal processing, feature engineering, and supervised machine learning methods.\"},{\"question\":\"How does the thesis evaluate machine learning models?\",\"answer\":\"It examines multiple pipelines: supervised classifiers for clinical measurements, supervised classifiers for patient self-measurements, an extension to acute decompensated heart failure detection, and deep neural networks for automated end-to-end feature learning.\"},{\"question\":\"What do the results indicate about SCG and GCG signals?\",\"answer\":\"SCG and GCG reliably capture the heart’s beating pattern regardless of the operator, enabling detection of irregular rhythm patterns suitable for remote monitoring outside hospital settings.\"}]","Machine Learning for the Classification of Atrial Fibrillation utilizing Seismo-and Gyrocardiogram | PDF",1785812822,217,{"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},"machine-learning-for-the-classification-of-atrial-fibrillation-utilizing-seismo-and-gyrocardiogram","",{"@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/machine-learning-for-the-classification-of-atrial-fibrillation-utilizing-seismo-and-gyrocardiogram/122773/",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","2026-08-04",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 classify atrial fibrillation using short seismo- and gyrocardiogram measurements recorded by smartphones, applying signal processing, feature engineering, and supervised machine learning methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis evaluate machine learning models?",{"text":80,"@type":76},"It examines multiple pipelines: supervised classifiers for clinical measurements, supervised classifiers for patient self-measurements, an extension to acute decompensated heart failure detection, and deep neural networks for automated end-to-end feature learning.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about SCG and GCG signals?",{"text":84,"@type":76},"SCG and GCG reliably capture the heart’s beating pattern regardless of the operator, enabling detection of irregular rhythm patterns suitable for remote monitoring outside hospital settings.","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"]