Ramachandran Anita, Karuppiah Anupama
Department of Computer Science & Information Systems, BITS, Pilani 560001, India.
Department of Electrical & Electronics Engineering, BITS, Pilani-K K Birla Goa Campus, Near NH17B, Zuari Nagar, Sancoale 403726, India.
Healthcare (Basel). 2021 Jul 20;9(7):914. doi: 10.3390/healthcare9070914.
Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.
睡眠呼吸暂停是一种影响大量人群的睡眠障碍。这种障碍会导致或加剧心血管功能障碍、中风、糖尿病以及生产力低下等问题。多导睡眠图（PSG）测试是检测睡眠呼吸暂停的金标准，但该测试费用高昂、不便操作，且广大民众无法进行。这就需要更友好、更易获得的睡眠呼吸暂停诊断解决方案。在本文中，我们研究了临床上如何检测睡眠呼吸暂停，以及嵌入式系统和机器学习的进展相结合如何有助于使睡眠呼吸暂停的诊断更简便、更经济且更易获得。我们阐述了机器学习在睡眠呼吸暂停检测中的相关性，并对上述领域的最新进展进行了研究。该综述涵盖了基于机器学习、深度学习和传感器融合的研究，并聚焦于睡眠呼吸暂停检测的以下几个方面：（i）用于数据收集的传感器类型，（ii）应用于数据的特征工程方法，（iii）用于睡眠呼吸暂停检测/分类的分类器。基于文献调查，我们还分析了睡眠呼吸暂停检测系统设计中的挑战。
