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.

项目总结 睡眠呼吸暂停是一种常见的慢性呼吸系统疾病，其特征是睡眠时呼吸困难。 诊断睡眠呼吸暂停的普遍临床实践需要手动识别呼吸暂停的发生，这 既昂贵又耗时。近年来，机器学习在呼吸暂停诊断中的应用受到广泛关注 基于通过可穿戴设备收集的生理信号。然而，现有的大多数研究都强烈依赖于 用于检测的监督学习和细粒度注释需要达到高水平的 粒度。在实际应用中，使用临时的方法获取大数据集通常是昂贵和耗时的 细粒度注释(即，在短时间内检测到呼吸暂停)。因此，有限的可用资源 细粒度标注阻碍了机器学习的广泛实现，限制了机器学习的粒度。 这项研究的最终目标是创建一个弱监督机器学习框架，该框架结合了 医疗数据分析的不同粒度级别和临床领域知识的注释。在……里面 尤其是深度学习，因为它表现出了优越的性能和巨大的潜力 帮助分析临床数据。拟议研究的技术目标是创建新的深度学习 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。 目的1.系统地识别和量化生理信号中与呼吸暂停相关的模式。这个 拟议的研究将从数值上探索生理信号，以阐明与呼吸暂停相关的模式 以及其他基于特征工程和统计学习技术的睡眠障碍。 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来整合不完整的细粒度 用于检测呼吸暂停的注释、粗粒度注释和临床知识。 目的3.提出了一种自适应获取标注的算法，提高了标注的性能。至 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来 确定是否以及在哪里从医生那里获得更多注释以及粒度级别。 拟议的研究将解决在不完整或没有预测的情况下生成细粒度预测的挑战 计算机辅助呼吸暂停检测中的细粒度标注。建议的模式将是对 结合粗粒度注释和领域知识的健壮且可解释的深度学习。 预期的研究结果将为解决其他生物医学领域的类似挑战提供重要的见解 应用程序，支持临床决策支持系统等新型现实解决方案，在家中 呼吸暂停监测和移动健康。