서브메뉴
검색
From Heartbeats to Algorithms: Robust Detection and Analysis of Cardio-Mechanical Signals Using Novel Machine Learning Algorithms and Wearable Technology
From Heartbeats to Algorithms: Robust Detection and Analysis of Cardio-Mechanical Signals Using Novel Machine Learning Algorithms and Wearable Technology
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105558
- ISBN
- 9798265400352
- DDC
- 150
- 서명/저자
- From Heartbeats to Algorithms: Robust Detection and Analysis of Cardio-Mechanical Signals Using Novel Machine Learning Algorithms and Wearable Technology
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 183 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Inan, Omer.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 요약This dissertation addresses critical challenges in cardiovascular health monitoring, focusing on the potential of cardiomechanical signals to improve early detection and management of Cardiovascular Disease (CVD). Given the worldwide prevalence of CVDs, the research underscores the importance of precision health and the role of wearable technology in revolutionizing patient monitoring. Through a series of scientific contributions, this work tackles key issues such as data scarcity, noise interference, and the need for sophisticated signal analysis techniques. First, we introduce deep generative models to augment cardiomechanical signal datasets, allowing for more extensive and diverse data for research and application development. We also describe the design of hardware phantoms to safely and reliably replicate human cardiomechanical signals for testing and validation purposes. Next, we introduce SeismoNet, a multi-node wearable platform that collects signals from multiple body points, improving signal quality and reducing noise and interference, enhancing the accuracy of health parameter estimations. Then, we design advanced deep learning models for denoising cardiomechanical signals, specifically addressing both stationary and non-stationary noise through architectures including U-Net and residual U-Net. Finally, we investigate a two-step approach of pre-training and task-specific optimization to refine the models for specific health monitoring tasks, demonstrating effectiveness in monitoring shunts in ductal dependent infants. By bridging the gaps associated with the utilization of cardiomechanical signals, the research within this dissertation paves the way for healthcare solutions that are more personalized, accurate, and accessible, aligned with the overarching goal of diminishing the global burden of cardiovascular diseases.
- 일반주제명
- Success
- 일반주제명
- Accelerometers
- 일반주제명
- Input output
- 일반주제명
- Electrocardiography
- 일반주제명
- Heart rate
- 일반주제명
- Medicine
- 일반주제명
- Physiology
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2024 us c eng d■001000017360632
■00520260202105558
■006m o d
■007cr#unu||||||||
■020 ▼a9798265400352
■035 ▼a(MiAaPQ)AAI32315927
■035 ▼a(MiAaPQ)GeorgiaTech75246
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a150
■1001 ▼aNikbakht, Mohammad.
■24510▼aFrom Heartbeats to Algorithms: Robust Detection and Analysis of Cardio-Mechanical Signals Using Novel Machine Learning Algorithms and Wearable Technology
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a183 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Inan, Omer.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aThis dissertation addresses critical challenges in cardiovascular health monitoring, focusing on the potential of cardiomechanical signals to improve early detection and management of Cardiovascular Disease (CVD). Given the worldwide prevalence of CVDs, the research underscores the importance of precision health and the role of wearable technology in revolutionizing patient monitoring. Through a series of scientific contributions, this work tackles key issues such as data scarcity, noise interference, and the need for sophisticated signal analysis techniques. First, we introduce deep generative models to augment cardiomechanical signal datasets, allowing for more extensive and diverse data for research and application development. We also describe the design of hardware phantoms to safely and reliably replicate human cardiomechanical signals for testing and validation purposes. Next, we introduce SeismoNet, a multi-node wearable platform that collects signals from multiple body points, improving signal quality and reducing noise and interference, enhancing the accuracy of health parameter estimations. Then, we design advanced deep learning models for denoising cardiomechanical signals, specifically addressing both stationary and non-stationary noise through architectures including U-Net and residual U-Net. Finally, we investigate a two-step approach of pre-training and task-specific optimization to refine the models for specific health monitoring tasks, demonstrating effectiveness in monitoring shunts in ductal dependent infants. By bridging the gaps associated with the utilization of cardiomechanical signals, the research within this dissertation paves the way for healthcare solutions that are more personalized, accurate, and accessible, aligned with the overarching goal of diminishing the global burden of cardiovascular diseases.
■590 ▼aSchool code: 0078.
■650 4▼aSuccess
■650 4▼aAccelerometers
■650 4▼aInput output
■650 4▼aElectrocardiography
■650 4▼aHeart rate
■650 4▼aMedicine
■650 4▼aPhysiology
■690 ▼a0800
■690 ▼a0564
■690 ▼a0719
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360632▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
