Summarizing Cardiac Data: An Automated Approach for Identifying Representative Heartbeats in the Clinical Setting

项目摘要/摘要 随着医院床边产生源源不断的患者数据，临床医生被要求解释这一点 数据与患者的医疗记录和实验室结果一起实时显示。拟议的项目提供了一种方法来 自动化临床决策支持(CDS)分析这些丰富数据中的一部分，重点放在时间上 心电序列汇总(TSS)与相关心电向量图的逼近 (VCG)在数据科学和应用数学的交界处使用技术。考虑到在床边 监测信号可能会受到噪声的干扰，区分噪声/伪影、心脏 心律失常和正常的心律失常；虽然研究这类问题的文献越来越多，但仍然有 需要为儿科人群解决这一问题--特别是对患有电击的儿科患者 心脏重症监护病房(CICU)可见传导异常。通过以下方式实现 中俄克拉荷马大学(UCO)和德克萨斯州贝勒医学院的研究人员 儿童医院(TCH)，该项目结合了深度学习算法和子集选择的应用 诸如离散经验插值法(DEM)之类的技术来分类和汇总所记录的数据 来自TCH的儿科CICU。具体地说，该项目的目标有两个：(1)应用变分 自动编码器(VAE)以区分噪声、心律失常和正常的窦性心律，以及(2)评估 现有和新开发的子集选择算法，并增加了对DeIM相关的强调 方法应用于心脏数据。UCO的本科生将评估VAE建筑的噪音 检测，执行模型选择，然后将选择的模型应用于患者数据以进行进一步分析。 将训练和选择更多的VAE模型以识别包含以下内容的心电和心电向量心电波形 病理学。VAE结果将与文献中现有方法生成的结果进行比较，并将 通知随后对患者数据的汇总。虽然迪姆已经在课堂上证明了自己的生存能力- 识别任务在以前的工作中，DeIM及其相关方法最初是为以下应用开发的 作为数学模型降阶，而不是类辨识。出于这个原因，学生们将进行必要的 DEM相关方法应用于各种数据类型的比较，特别关注实验 涉及到心电波形；在这样做的同时，学生还将开发一种量身定做的这种方法的新扩展 在这个特定的医学背景下。此外，将这些技术用于类别识别目的的比较 将为更大的生物医学信息学和数据提供关于DeIM相关方法的有价值的见解 科学界。一旦建立，这个TSS框架将提供一种向临床医生展示 表示患者最近的心脏健康史，可以按原样使用的信息，也可以作为输入 其他预测模型。如果实现了这一长期目标，将为改善患者预后提供CDS，同时 让本科生有机会参与创新的跨学科研究。

Project Summary/Abstract With a constant stream of patient data generated at the hospital bedside, clinicians are asked to interpret this data along with patient medical records and lab results in real time. The proposed project offers an approach to automated clinical decision support (CDS) in parsing through some of this abundant data, focusing on the time series summarization (TSS) of the electrocardiogram (ECG) and approximations to the related vectorcardiogram (VCG) using techniques at the interface of data science and applied mathematics. Given the fact that bedside monitor signals can be corrupted by noise, it is important to distinguish between noise/artifact, cardiac arrhythmia, and normal cardiac rhythms; while the literature approaching such issues is growing, there is still a need for addressing this problem for the pediatric population – especially for pediatric patients with electrical conduction abnormalities as seen in the Cardiac Intensive Care Unit (CICU). Through collaboration between investigators at the University of Central Oklahoma (UCO) and at Baylor College of Medicine and Texas Children’s Hospital (TCH), this project combines the application of deep learning algorithms and subset selection techniques such as the discrete empirical interpolation method (DEIM) to classify and summarize data recorded from the pediatric CICU at TCH. Specifically, the objective of this project is two-fold: (1) apply variational autoencoders (VAEs) to differentiate between noise, arrhythmias, and normal sinus rhythm, and (2) evaluate both existing and newly developed subset selection algorithms, with an added emphasis on DEIM-related methods in application to cardiac data. Undergraduate students at UCO will evaluate VAE architectures for noise detection, performing model selection and then applying the chosen model to patient data for further analysis. Additional VAE models will be trained and selected for recognizing ECG and VCG waveforms containing pathologies. VAE results will be compared to those generated using existing methods in the literature and will inform the subsequent summarization of patient data. While DEIM has demonstrated viability in class- identification tasks in prior work, DEIM and its related methods were originally developed for applications such as mathematical model reduction, not class identification. For this reason, students will perform a necessary comparison of DEIM-related methods applied to a variety of data types, giving particular attention to experiments involving ECG waveforms; while doing so, students will also develop a novel extension of such methods tailored to this specific medical context. In addition, the comparison of these techniques for class identification purposes will offer valuable insight regarding DEIM-related methods to both the larger biomedical informatics and data science communities. Once established, this TSS framework will provide a means of presenting to clinicians a representation of a patient’s recent cardiac health history, information that can be used as-is and as input to other predictive models. If met, this long-term goal will provide CDS toward improving patient outcomes while giving undergraduate students an opportunity to participate in innovative interdisciplinary research.