UTHSC-ORNL, Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN 38103, USA; Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA.
Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA; Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN 38103, USA.
Int J Med Inform. 2017 Dec;108:55-63. doi: 10.1016/j.ijmedinf.2017.09.006. Epub 2017 Sep 20.
A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals.
PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals.
An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools.
及时诊断充血性心力衰竭（CHF）对于避免危及生命的事件至关重要。本文提出了一种新颖的概率符号模式识别（PSPR）方法，用于从心脏间（R-R）间隔检测 CHF 患者。
PSPR 通过将每个连续的 R-R 间隔时间序列映射到八个符号字母表上来对其进行离散化，然后对序列符号表示中的模式转换行为进行建模。对 107 名受试者（69 名正常和 38 名 CHF 受试者）的离散序列进行基于 PSPR 的分析，得出了可区分正常受试者和 CHF 受试者的可辨特征。除了 PSPR 特征外，我们还使用时域心率变异性测量值（例如 R-R 间隔的平均值和标准差）提取特征。
使用袋装决策树集合对两组进行分类，得到五折交叉验证的准确率、特异性和灵敏度分别为 98.1%、100%和 94.7%。但是，20%的保留验证的准确率、特异性和灵敏度分别为 99.5%、100%和 98.57%。这项研究的结果表明，结合 PSPR 和长期心率变异性测量值获得的特征可用于开发自动化 CHF 诊断工具。
