Joergensen Villads Hulgaard, Hanif Umaer, Jennum Poul, Mignot Emmanuel, Helge Asbjoern W, Sorensen Helge B D
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:164-168. doi: 10.1109/EMBC46164.2021.9629624.
Annotation of polysomnography (PSG) recordings for diagnosis of obstructive sleep apnea (OSA) is a standard procedure but an expensive and time-consuming process for clinicians. To aid clinicians in this process we present a data driven unsupervised hierarchical clustering approach for detection and visual presentation of breathing patterns in PSG recordings. The aim was to develop a model independent of manual annotations to detect and visualize respiratory events related to OSA. 10 recordings from the Sleep Heart Health Study database were used, and the proposed algorithm was evaluated based on the manually annotated events for each recording. The algorithm reached an F1-score of 0.58 across the 10 recordings when detecting the presence of an event vs. no event and a 100% correct diagnosis prediction of OSA when predicting if apnea-hypopnea index (AHI) ≥ 15, which is a clinically meaningful cut-off. The F1-score may be due to imprecise placement of events, difficulty distinguishing between hypopneas and stable breathing, and variations in scoring. In conclusion the performance can be improved despite the strong agreement in diagnostics. The method is a proof of concept that a clustering method can detect and visualize breathing patterns related to OSA while maintaining a correct diagnosis.
对多导睡眠图（PSG）记录进行注释以诊断阻塞性睡眠呼吸暂停（OSA）是一种标准程序，但对临床医生来说是一个昂贵且耗时的过程。为了在这个过程中帮助临床医生，我们提出了一种数据驱动的无监督层次聚类方法，用于检测和可视化PSG记录中的呼吸模式。目的是开发一种独立于人工注释的模型，以检测和可视化与OSA相关的呼吸事件。使用了来自睡眠心脏健康研究数据库的10份记录，并根据每份记录的人工注释事件对所提出的算法进行了评估。在检测事件是否存在时，该算法在10份记录中的F1分数达到了0.58，在预测呼吸暂停低通气指数（AHI）≥15（这是一个具有临床意义的临界值）时，对OSA的诊断预测正确率达到了100%。F1分数可能是由于事件放置不准确、难以区分低通气和稳定呼吸以及评分差异所致。总之，尽管在诊断方面有很强的一致性，但性能仍可提高。该方法是一个概念验证，即聚类方法可以检测和可视化与OSA相关的呼吸模式，同时保持正确的诊断。
