[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125077-en":3,"doc-seo-125077-105":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},125077,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Towards Cardiopulmonary Resuscitation Automation using Machine Learning - Thesis","Cardiopulmonary resuscitation (CPR) is a time-critical intervention intended to restore vital blood circulation and breathing in patients experiencing cardiac arrest or respiratory failure. This work reviews and analyzes existing machine learning (ML) roles in CPR, focusing on automated signal-based pattern identification and decision support across multiple CPR-related tasks. The study highlights research gaps, especially the lack of dedicated ML unsupervised denoising methods despite the fact that CPR signals are frequently corrupted by complex noise. To address this, a tailored multi-modality ML framework is proposed for unsupervised denoising of biomedical signals under realistic scenarios, improving noise removal, signal fidelity, and preserving signal correlations for downstream tasks.","Towards Cardiopulmonary Resuscitation Automation  \nusing Machine Learning  \nSaidul Islam  \nA Thesis  \nin  \nThe Department  \nof  \nConcordia Institute for Information Systems Engineering  \nPresented in Partial Fulfillment of the Requirements  \nfor the Degree of  \nMaster of Applied Science (Quality Systems Engineering) at Concordia University  \nMontral, Qubec, Canada  \nAugust 2024  \n© Saidul Islam, 2024  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Saidul Islam  \nEntitled: Towards Cardiopulmonary Resuscitation Automation using Machine  \nLearning  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Applied Science (Quality Systems Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Rachida Dssouli  \n  Examiner  \nDr. Nizar Bouguila  \nDr. Jamal Bentahar  Supervisor  \nApproved by  Dr. Chun Wang, Chair  Department of Concordia Institute for Information Systems Engineering  \n~~ ~~ 2024  Dr. Mourad Debbabi, Dean   \nFaculty of Engineering and Computer Science  \nAbstract  \nTowards Cardiopulmonary Resuscitation Automation using Machine Learning  \nSaidul Islam  \nCardiopulmonary resuscitation (CPR) is a critical intervention aimed at restoring vital blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure, representing a crucial aspect of emergency medical care. Numerous biomedical signals are associated with CPR execution and monitoring, from initial out-of-hospital treatment to the hospital’s intensive care unit (ICU) . Machine learning (ML) can play a crucial role in automating the CPR process by utilizing signals to identify complex patterns and in decision-making. In this context, we explored the existing role of ML in CPR and analyzed current ML approaches for various CPR-related tasks in this thesis. Our review highlights research gaps and sets new directions for empirical studies, uncovering the unexplored potential of ML applications in CPR. Through the analysis, we identified that CPR signals often suffer from noise, complicating accurate clinical interpretation and decision-making. Conventional denoising methods using filters exhibit limitations in addressing the complex noise characteristics inherent in CPR signals. Although ML is known for handling complex data characteristics, a dedicated ML-based unsupervised approach for denoising CPR signals is still missing. To this end, we proposed a novel ML framework tailored for denoising biomedical signals during CPR. Utilizing a multi-modality approach, our framework leverages a dedicated ML algorithm for individual signals while concurrently denoising multiple signals through unsupervised ML approaches considering real-life scenarios. Our framework demonstrates significant noise removal and signal fidelity enhancements. Furthermore, our methodology preserves signal correlations, essential for downstream tasks. Finally, the proposed framework aims to improve CPR monitoring and decision-making, offering adaptability and extensibility to denoise a range of biomedical signals  \nbeyond CPR scenarios.  \nAcknowledgments  \nFirst and foremost, I would like to express my sincere gratitude to my supervisor Dr. Jamal Bentahar, for their invaluable support, and feedback and keep believing in me throughout my graduate program. Your endless encouragement and precious guidance have been instrumental in my journey, and I could not have undertaken this without you. Thank you very much.  \nMoreover, I would like to thank Doctor Lawrence Leroux for sharing his expertise in cardiac arrest and helping me with domain knowledge and suggestions. Furthermore, I am thankful to Dr. Gaith Rjoub for his encouragement.  \nI extend my deepest gratitude to my beloved parents, and family members for their unwavering love, emotional, and spiritu","cbCaiiUgiUIQlTDU","https://ap.wps.com/l/cbCaiiUgiUIQlTDU","pdf",2015499,1,84,"English","en",105,"# List of Figures\n# List of Tables\n# 1 Introduction\n## 1.1 Context\n## 1.2 Background\n## 1.3 Challenges and Motivation\n## 1.4 Contributions\n## 1.5 Thesis Outline\n# 2 The Role of Machine Learning in Enhancing Resuscitation Techniques\n## 2.1 A Classification of ML Approaches for CPR Tasks\n## 2.2 Rhythm Analysis\n## 2.3 Outcome Prediction\n## 2.4 Non-Invasive Blood Pressure and Chest Compression\n## 2.5 Pulse and Return Of Spontaneous Circulation Detection (ROSC)\n## 2.6 Machine Learning Approaches for Other CPR Tasks\n# 3 A Multi-Modal Machine Learning Framework for Denoising Biomedical CPR Signals\n## 3.1 Context\n## 3.2 Related Work\n## 3.3 Preliminaries\n## 3.4 Methodology and Framework\n# 4 Experiment Set-Up\n## 4.1 Data Preparation","[{\"question\":\"What problem does the thesis address in CPR automation?\",\"answer\":\"It targets the use of machine learning to automate CPR-related monitoring and decision-making using biomedical signals, while addressing the difficulty of accurate interpretation caused by noisy CPR signals.\"},{\"question\":\"Why is denoising CPR signals challenging with conventional methods?\",\"answer\":\"Traditional filter-based denoising methods have limitations because CPR signals contain complex noise characteristics that are difficult to model with conventional approaches.\"},{\"question\":\"What is the proposed solution and what does it achieve?\",\"answer\":\"The thesis proposes a multi-modality ML framework that performs unsupervised denoising for biomedical CPR signals, removing noise effectively, enhancing signal fidelity, and preserving signal correlations for downstream tasks.\"}]","Towards Cardiopulmonary Resuscitation Automation using Machine Learning - Thesis | PDF",1785896499,212,{"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},"towards-cardiopulmonary-resuscitation-automation-using-machine-learning-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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/towards-cardiopulmonary-resuscitation-automation-using-machine-learning-thesis/125077/",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 problem does the thesis address in CPR automation?","Question",{"text":75,"@type":76},"It targets the use of machine learning to automate CPR-related monitoring and decision-making using biomedical signals, while addressing the difficulty of accurate interpretation caused by noisy CPR signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is denoising CPR signals challenging with conventional methods?",{"text":80,"@type":76},"Traditional filter-based denoising methods have limitations because CPR signals contain complex noise characteristics that are difficult to model with conventional approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the proposed solution and what does it achieve?",{"text":84,"@type":76},"The thesis proposes a multi-modality ML framework that performs unsupervised denoising for biomedical CPR signals, removing noise effectively, enhancing signal fidelity, and preserving signal correlations for downstream tasks.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]