Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:135-138. doi: 10.1109/EMBC46164.2021.9630574.
This work addresses the automatic segmentation of neonatal phonocardiogram (PCG) to be used in the artificial intelligence-assisted diagnosis of abnormal heart sounds. The proposed novel algorithm has a single free parameter - the maximum heart rate. The algorithm is compared with the baseline algorithm, which was developed for adult PCG segmentation. When evaluated on a large clinical dataset of neonatal PCG with a total duration of over 7h, an F1 score of 0.94 is achieved. The main features relevant for the segmentation of neonatal PCG are identified and discussed. The algorithm is able to increase the number of cardiac cycles by a factor of 5 compared to manual segmentation, potentially allowing to improve the performance of heart abnormality detection algorithms.
这项工作旨在实现新生儿心音图（PCG）的自动分割，以便在人工智能辅助诊断异常心音中使用。所提出的新算法只有一个自由参数 - 最大心率。该算法与基线算法进行了比较，基线算法是为成人 PCG 分割而开发的。在一个包含超过 7 小时总时长的大型新生儿 PCG 临床数据集上进行评估时，获得了 0.94 的 F1 分数。确定并讨论了与新生儿 PCG 分割相关的主要特征。与手动分割相比，该算法能够将心搏周期数增加 5 倍，这可能有助于提高心脏异常检测算法的性能。
