Knowledge-informed Deep Learning for Apnea Detection with Limited Annotations

用于具有有限注释的呼吸暂停检测的知识型深度学习

• 批准号: 10509437
• 负责人: Changyue Song
• 金额: $ 16.05万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10509437
• 关键词: Address; Adopted; Affect; Air Movements; Algorithms; Apnea; Arousal; Attention; Breathing; Clinical; Clinical Data; Complex; Computer Assisted; Consumption; Data; Data Analytics; Detection; Diagnosis; Engineering; Event; Goals; Grain; Health; Healthcare; Home; Knowledge; Learning; Machine Learning; Manuals; Modeling; Monitor; Noise; Oxygen; Patients; Pattern; Performance; Physicians; Physiological; Polysomnography; Probability; Process; Records; Research; Severities; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Disorders; Support System; Techniques; Time; United States; Woman; base; chronic respiratory disease; clinical decision support; clinical decision-making; clinical practice; cost effective; deep learning; deep learning model; detection method; improved; indexing; insight; large datasets; mHealth; machine learning framework; men; novel; patient oriented; statistical learning; supervised learning; wearable device

PROJECT SUMMARY Sleep apnea is a common chronic respiratory disease characterized by breathing difficulties during sleep. Prevalent clinical practice to diagnose sleep apnea requires manual identification of apnea occurrences, which is expensive and time-consuming. Recently, machine learning has attracted much attention to diagnose apnea based on physiological signals collected via wearable devices. However, most existing studies rely on strongly supervised learning for the detection, and fine-grained annotations are required to achieve a high level of granularity. In practice, it is usually expensive and time-consuming to acquire a large dataset with temporally fine-grained annotations (i.e., detecting apnea within short time epochs). Consequently, the limited availability of fine-grained annotations hinders the wide implementation of machine learning and limits its granularity. The ultimate goal of this research is to create a weakly-supervised machine learning framework that incorporates annotations of different granularity levels and clinical domain knowledge for healthcare data analytics. In particular, this study focuses on deep learning because it has shown superior performance and great potential in aiding the analysis of clinical data. The technical objective of the proposed study is to create new deep learning models that incorporate coarse-grained annotations and clinical knowledge for detecting apnea at a high level of granularity based on multiple physiological signals. The specific aims of this proposal are as follows. Aim 1. Systematically identify and quantify the apnea-related patterns in physiological signals. The proposed study will numerically explore the physiological signals to elucidate the patterns related to apnea and other sleep disorders based on feature engineering and statistical learning techniques. Aim 2. Incorporate coarse-grained annotations and clinical knowledge into deep learning models for apnea detection. We will establish new deep learning models to integrate incomplete fine-grained annotations, coarse-grained annotations, and clinical knowledge for apnea detection. Aim 3. Develop an algorithm to adaptively acquire annotations for performance improvement. To further improve the performance of the deep learning model, we will develop an adaptive algorithm to determine whether and where to acquire more annotations from physicians and the level of granularity. The proposed study will address the challenge of generating fine-grained predictions given incomplete or no fine-grained annotations in computer-aided apnea detection. The proposed model will be an advancement to robust and interpretable deep learning that incorporates coarse-grained annotations and domain knowledge. The expected results of study will provide important insights in addressing similar challenges in other biomedical applications, enabling novel real-world solutions such as clinical decision-making support systems, in-home apnea monitoring, and mobile health.

项目摘要 睡眠呼吸暂停是一种常见的慢性呼吸系统疾病，其特征是睡眠期间呼吸困难。 诊断睡眠呼吸暂停的普遍临床实践需要人工识别呼吸暂停的发生， 是昂贵且耗时的。最近，机器学习在诊断呼吸暂停方面备受关注 基于通过可穿戴设备收集的生理信号。然而，大多数现有的研究都强烈依赖于 监督学习的检测，和细粒度的注释需要实现高水平的 粒度在实践中，通常是昂贵的和耗时的，以获取一个大的数据集， 细粒度注释（即，在短时间段内检测呼吸暂停）。因此， 细粒度的注释阻碍了机器学习的广泛实现并限制了其粒度。 这项研究的最终目标是创建一个弱监督机器学习框架， 用于医疗保健数据分析的不同粒度级别的注释和临床领域知识。在 特别是，这项研究重点关注深度学习，因为它已经显示出上级性能和巨大潜力 帮助分析临床数据。这项研究的技术目标是创造新的深度学习。 结合粗粒度注释和临床知识的模型，用于在高水平上检测呼吸暂停 基于多个生理信号的粒度。这项建议的具体目标如下。 目标1.系统地识别和量化生理信号中的呼吸暂停相关模式。的 拟议的研究将数值探索生理信号，以阐明与呼吸暂停相关的模式 以及基于特征工程和统计学习技术的其他睡眠障碍。 目标2.将粗粒度注释和临床知识整合到深度学习模型中， 呼吸暂停检测。我们将建立新的深度学习模型，以整合不完整的细粒度 注释、粗粒度注释和用于呼吸暂停检测的临床知识。 目标3.开发一种算法来自适应地获取注释以提高性能。到 为了进一步提高深度学习模型的性能，我们将开发一种自适应算法， 确定是否以及在何处从医生处获取更多注释以及粒度级别。 拟议的研究将解决在不完整或不完整的情况下生成细粒度预测的挑战。 在计算机辅助呼吸暂停检测中的细粒度注释。拟议的模式将是一个进步， 鲁棒且可解释的深度学习，包含粗粒度注释和领域知识。 研究的预期结果将为解决其他生物医学领域的类似挑战提供重要见解。 应用程序，实现新的现实世界的解决方案，如临床决策支持系统， 呼吸暂停监测和移动的健康。