Automated (AI) Analysis of Sleep Disordered Breathing

睡眠呼吸障碍的自动 (AI) 分析

• 批准号: 6320670
• 负责人: INDU A AYAPPA
• 金额: $ 14.75万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6320670
• 关键词: artificial intelligence; automated data processing; breath tests; cardiopulmonary disease; computer program /software; computer system design /evaluation; diagnosis design /evaluation; diagnosis quality /standard; disease /disorder classification; electrocardiography; electroencephalography; electromyography; electrooculography; human subject; noninvasive diagnosis; patient oriented research; polysomnography; respiratory airflow disorder; respiratory airflow measurement; respiratory disorder diagnosis; sleep disorders

DESCRIPTION (provided by applicant) This training grant is designed to advance the academic career of Dr. Indu Ayappa by building on the applicant?s strong biomedical engineering and computer background and providing comprehensive multi-disciplinary training which will allow her to become an independent investigator. The applicant?s career goals are to enter full time academic research in sleep physiology. Training activities proposed will include academic course work designed to support the research program and expose her to aspects of clinical research in sleep, neural science and artificial intelligence techniques. She will be mentored by David Rapoport, M.D., Joyce Walsleben, Ph.D., and Maurice Ohayon, M.D., Ph.D., as well as faculty in computer science and neurophysiology. The aims of the planned research are to develop an artificial intelligence system for the identification and quantification of sleep disordered breathing (SDB) based solely on non-invasive cardiopulmonary signals collected during routine polysomnography. This will simplify, standardize and improve the diagnosis of SDB and facilitate research in this area. With the long term goal of physiologic characterization of the spectrum of SDB in order to clinically diagnose upper airway resistance syndrome (UARS), the aims of this project are to: 1. Extract features from the nasal cannula airflow signal to characterize the state of resistance/collapsibility of individual breaths. These include amplitude, inspiratory flow contour, Ti/Ttot and presence of vibration which will be used as inputs to a neural network that will be trained and evaluated against breaths that have been classified by reference measurement of upper airway resistance (pressure/flow). 2A. Incorporate information from this classification of individual breaths to detect and classify respiratory events based on the flow signal alone using a trained neural network. 2B. Evaluate the utility of including additional cardiopulmonary signals like oxygen saturation, heart rate, pulse transit time and rib/abdominal movements (amplitude and phase) in the detection and classification of these events. Successful completion of the training and research program will allow Dr. Ayappa to contribute independently to research in the field of sleep physiology.

描述（由申请人提供） 这笔培训补助金旨在促进 Indu 博士的学术生涯 Ayappa 以申请人强大的生物医学工程和 计算机背景并提供全面的多学科培训 这将使她成为一名独立调查员。申请人的 职业目标是全职从事睡眠生理学学术研究。 拟议的培训活动将包括旨在 支持研究计划并使她接触到临床研究的各个方面 睡眠、神经科学和人工智能技术。她将会是 由 David Rapoport 医学博士、Joyce Walsleben 博士和 Maurice Ohayon 指导， 医学博士、博士，以及计算机科学和神经生理学教师。这 计划研究的目标是开发人工智能系统 用于识别和量化睡眠呼吸障碍 (SDB) 仅基于常规期间收集的无创心肺信号 多导睡眠图。这将简化、标准化和改进诊断 SDB 并促进该领域的研究。长期目标是 SDB 谱的生理学特征，以便在临床上 诊断上气道阻力综合征（UARS），该项目的目标是 1. 从鼻插管气流信号中提取特征来表征 个体呼吸的阻力/塌陷状态。这些包括 振幅、吸气流量轮廓、Ti/Ttot 和振动的存在 将用作神经网络的输入，该神经网络将被训练和评估 对照通过上颌参考测量进行分类的呼吸 气道阻力（压力/流量）。 2A。合并来自此的信息 对个体呼吸进行分类以检测和分类呼吸事件 使用经过训练的神经网络仅基于流量信号。 2B。评价 包括额外的心肺信号（如氧气）的效用 饱和度、心率、脉搏传导时间和肋骨/腹部运动 （幅度和相位）用于这些事件的检测和分类。 培训和研究计划的成功完成将使博士能够 阿亚帕将独立为睡眠领域的研究做出贡献 生理。