Knowledge-informed Deep Learning for Apnea Detection with Limited Annotations

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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.