AUTOMATED (AI) ANALYSIS OF SLEEP DISORDERED BREATHING

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 该项目的目标是实现一个人工智能(AI)系统，以识别和量化睡眠期间的呼吸事件，基于常规多导睡眠图收集的无创性心肺信号。该项目的具体目标是从鼻腔插管气流信号中提取表征个体呼吸阻力/塌陷状态的特征。这些将被用作神经网络的输入，该神经网络将根据已通过参考测量上呼吸道阻力进行分类的呼吸进行训练和评估。由于在“呼吸事件”期间通常存在一簇簇的“异常”呼吸，研究人员建议将来自个体呼吸分类的信息结合起来，以使用训练好的神经网络仅基于流量信号来检测和分类呼吸事件。还将评估心肺信号在检测和分类这些事件中的作用。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The objective of this project is to implement an artificial intelligence (AI) system for the identification and quantification of the respiratory events during sleep, based on noninvasive cardiopulmonary signals collected during routine polysomnography. The specific aims of the project are to extract features from the nasal cannula airflow signal that characterize the state of resistance/collapsibility of individual breaths. These 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. Since clusters of "abnormal" breaths are generally present during "respiratory events," the investigator proposes to incorporate information from this classification of individual breaths to detect and classify respiratory events based on flow signal alone using a trained neural network. Utility of cardiopulmonary signals in the detection and classification of these events will also be evaluated.