de Oliveira Lorena S C, Andreão Rodrigo V, Sarcinelli-Filho Mario
Science e Technology Institute, Federal University of Vales do Jequitinhonha e Mucuri, Teófilo Otoni, Minas Gerais, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4984-7. doi: 10.1109/IEMBS.2011.6091235.
This paper investigates the viability of using the dynamic Bayesian Network framework as a tool to classify heart beats in long term ECG records. A Decision Support System composed by two layers is considered. The first layer performs the segmentation of each heartbeat available in the ECG record, whereas the second layer classifies the heartbeat as Premature Ventricular Contraction (PVC) or Other. The use of both static and dynamic Bayesian Networks is evaluated through using the records available in the MIT-BIH database, and the results show that the Dynamic one performs better, obtaining 95% of sensitivity and 98% of positive predictivity, showing that to consider the temporal relation among events is a good strategy to increase the certainty about present events.
本文研究了使用动态贝叶斯网络框架作为工具对长期心电图记录中的心跳进行分类的可行性。考虑了一个由两层组成的决策支持系统。第一层对心电图记录中可用的每个心跳进行分割，而第二层将心跳分类为室性早搏（PVC）或其他类型。通过使用麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）数据库中的记录，对静态和动态贝叶斯网络的使用进行了评估，结果表明动态贝叶斯网络表现更好，灵敏度达到95%，阳性预测值达到98%，这表明考虑事件之间的时间关系是提高对当前事件确定性的一个好策略。
