用于检测短时长胎儿心率记录中胎儿心律失常的熵分析
Entropy Profiling for Detection of Fetal Arrhythmias in Short Length Fetal Heart Rate Recordings.
作者信息
Keenan Emerson, Udhayakumar Radhagayrathi K, Karmakar Chandan K, Brownfoot Fiona C, Palaniswami Marimuthu
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:621-624. doi: 10.1109/EMBC44109.2020.9175892.
The use of fetal heart rate (FHR) recordings for assessing fetal wellbeing is an integral component of obstetric care. Recently, non-invasive fetal electrocardiography (NI-FECG) has demonstrated utility for accurately diagnosing fetal arrhythmias via clinician interpretation. In this work, we introduce the use of data-driven entropy profiling to automatically detect fetal arrhythmias in short length FHR recordings obtained via NI-FECG. Using an open access dataset of 11 normal and 11 arrhythmic fetuses, our method (TotalSampEn) achieves excellent classification performance (AUC = 0.98) for detecting fetal arrhythmias in a short time window (i.e. under 10 minutes). We demonstrate that our method outperforms SampEn (AUC = 0.72) and FuzzyEn (AUC = 0.74) based estimates, proving its effectiveness for this task. The rapid detection provided by our approach may enable efficient triage of concerning FHR recordings for clinician review.
使用胎儿心率（FHR）记录来评估胎儿健康状况是产科护理的一个重要组成部分。最近，无创胎儿心电图（NI-FECG）已显示出通过临床医生解读准确诊断胎儿心律失常的效用。在这项工作中，我们介绍了使用数据驱动的熵分析方法，以自动检测通过NI-FECG获得的短时长FHR记录中的胎儿心律失常。利用一个包含11例正常胎儿和11例心律失常胎儿的开放获取数据集，我们的方法（TotalSampEn）在短时间窗口（即10分钟以内）检测胎儿心律失常方面实现了出色的分类性能（AUC = 0.98）。我们证明，我们的方法优于基于样本熵（SampEn，AUC = 0.72）和模糊熵（FuzzyEn，AUC = 0.74）的估计方法，证明了其在此任务中的有效性。我们的方法提供的快速检测可能有助于对令人担忧的FHR记录进行有效分诊，以供临床医生审查。
Zotero 插件