[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125160-en":3,"doc-seo-125160-105":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},125160,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","EFFICIENT MACHINE LEARNING ALGORITHMS FOR ONE-AND TWO-DIMENSIONAL BIOMEDICAL SIGNALS - Dissertation","Healthcare benefits from advances in data computing and machine learning to extract actionable insights from large biomedical datasets, including records, medical imaging, genomic sequencing, and clinical trials. This dissertation introduces machine-learning methods for detecting life-threatening ventricular arrhythmias using statistical features across varying ECG durations, achieving high performance against normal rhythm. It also proposes single-lead ST-segment depression detection by transforming 1D ECG into 2D images, and explores ultrasound image augmentation with GANs to improve breast-cancer detection.","EFFICIENT MACHINE LEARNING ALGORITHMS FOR ONE-AND  \nTWO-DIMENSIONAL BIOMEDICAL SIGNALS  \nby  \nErhan Tiryaki  \nAPPROVED BY SUPERVISORY COMMITTEE:  \n\n| Lakshman Tamil, Chair |\n| --- |\n| Andrea Fumagalli |\n| Mehrdad Nourani |\n\nKatherine Brown  \nCopyright © 2023 Erhan Tiryaki All rights reserved  \nThis dissertation is dedicated to my family, whose unwavering support and prayers have been instrumental in my success.  \nEFFICIENT MACHINE LEARNING ALGORITHMS FOR ONE-AND TWO-DIMENSIONAL BIOMEDICAL SIGNALS  \nby  \nERHAN TIRYAKI, BS, MS  \nDISSERTATION  \nPresented to the Faculty of  \nThe University of Texas at Dallas  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDOCTOR OF PHILOSOPHY IN  \nTELECOMMUNICATIONS ENGINEERING  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nDecember 2023  \nACKNOWLEDGMENTS  \nExpressing gratitude is an essential part of life, and it is my pleasure to acknowledge Dr. Lakshman S. Tamil, my advisor during my doctoral studies, who has played an integral role in my academic journey. Dr. Tamil’s passion for conducting high-quality research has been truly inspiring, and I am grateful for the valuable lessons he has imparted on me. Without his unwavering support and guidance, I could not have completed my research in a timely and efficient manner.  \nI would also like to thank the other members of my supervisory committee, Dr. Andrea Fumagalli, Dr. Mehrdad Nourani, Dr. Katherine Brown, and External Chair Dr. Rym Zalila-Wenkstern, for their patience and constructive feedback throughout my dissertation and research.  \nI would like to extend my appreciation to my friends, especially Osman Murat Kutlu and ˙Ishak Yıldız, for their unrelenting support throughout my journey. Moreover, I would like to sincerely thank the Ministry of National Education of the Republic of T¨urkiye for providing me with the opportunity to attend such a prestigious university for graduate-level education.  \nFinally, I would like to express my gratitude to the Erik Jonsson School of Engineering and Computer Science for providing me with the Graduate Teaching Assistantship for several semesters during my doctoral studies at UTD.  \nNovember 2023  \nEFFICIENT MACHINE LEARNING ALGORITHMS FOR ONE-AND  \nTWO-DIMENSIONAL BIOMEDICAL SIGNALS  \nErhan Tiryaki, PhD  \nThe University of Texas at Dallas, 2023  \nSupervising Professor: Lakshman Tamil, Chair  \nData analysis plays a crucial role in healthcare when it comes to diagnosing and detecting illnesses and medical conditions. Thanks to the advancements in data computing and machine learning, healthcare professionals can leverage this technology to their advantage. There is a vast amount of biomedical data available, ranging from patient records to medical imaging and genomic sequencing, as well as clinical trial results. By analyzing this data with the help of machine learning, healthcare professionals can gain valuable insights that could lead to more accurate diagnoses, better treatment options, and improved health outcomes for patients. It’s worth noting that any discovery made through data analysis has the potential to enhance the quality of life for individuals.  \nThis dissertation presents a successful method for detecting life-threatening ventricular arrhythmias, namely ventricular tachycardia, ventricular fibrillation, and ventricular flutter, through the use of machine learning algorithms. The method leverages various statistical features and is capable of detecting these arrhythmias over different ECG signal durations. Our method can efficiently differentiate ventricular tachycardia/fibrillation/flutter (VTFL) against normal sinus rhythm (NSR) with an accuracy, recall and positive predictive value of 98.21, 95 .57 and 98 .61 percents respectively. The discriminatory power of the same algorithm  \nbetween VTFL and non-VTFL as characterized by accuracy, recall and positive predictive value are 98.12, 93.29, and 97.2 percents respectively.  \nThis dissertation also suggests a novel way to identify ST","cbCaiiODbo5DnrfZ","https://ap.wps.com/l/cbCaiiODbo5DnrfZ","pdf",4514313,1,98,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1 Introduction","[{\"question\":\"What ventricular arrhythmias does the dissertation target, and how are they detected?\",\"answer\":\"The dissertation targets ventricular tachycardia, ventricular fibrillation, and ventricular flutter. Detection uses machine learning algorithms leveraging statistical features applied to ECG signals of different durations.\"},{\"question\":\"How does the dissertation detect ST-segment depression using a single ECG lead?\",\"answer\":\"It transforms one-dimensional ECG signals into two-dimensional images, then applies a convolutional neural network to detect ST-segment depression as an indicator of myocardial ischemia.\"},{\"question\":\"How are GANs used for breast cancer detection in this work?\",\"answer\":\"Ultrasound images are augmented using Generative Adversarial Networks to improve detection performance, yielding higher accuracy than a single-model approach.\"}]","EFFICIENT MACHINE LEARNING ALGORITHMS FOR ONE-AND TWO-DIMENSIONAL BIOMEDICAL SIGNALS - Dissertation | PDF",1785897070,247,{"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},"efficient-machine-learning-algorithms-for-one-and-two-dimensional-biomedical-signals-dissertation","",{"@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/efficient-machine-learning-algorithms-for-one-and-two-dimensional-biomedical-signals-dissertation/125160/",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 ventricular arrhythmias does the dissertation target, and how are they detected?","Question",{"text":75,"@type":76},"The dissertation targets ventricular tachycardia, ventricular fibrillation, and ventricular flutter. Detection uses machine learning algorithms leveraging statistical features applied to ECG signals of different durations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation detect ST-segment depression using a single ECG lead?",{"text":80,"@type":76},"It transforms one-dimensional ECG signals into two-dimensional images, then applies a convolutional neural network to detect ST-segment depression as an indicator of myocardial ischemia.",{"name":82,"@type":73,"acceptedAnswer":83},"How are GANs used for breast cancer detection in this work?",{"text":84,"@type":76},"Ultrasound images are augmented using Generative Adversarial Networks to improve detection performance, yielding higher accuracy than a single-model approach.","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"]