Hu Jing, Zhao Wei, Jia Dongya, Yan Cong, Wang Hongmei, Li Zhenqi, Fang Jiansheng, Yang Ming
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:353-356. doi: 10.1109/EMBC44109.2020.9175909.
Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat: right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.
束支传导阻滞（BBB）是最常见的心脏疾病之一，在临床实践中可通过心电图（ECG）信号检测到。传统方法采用了某些手工特征，其判别能力相对较低。另一方面，这些方法基于监督学习，在训练中需要高昂的心跳标注成本。在本文中，提出了一种新颖的端到端深度网络，采用基于多实例学习的训练策略对三种心跳类型进行分类：右束支传导阻滞（RBBB）、左束支传导阻滞（LBBB）和其他类型。我们在中国生理信号挑战赛2018数据库（CPSC）上训练了所提出的方法，并在麻省理工学院-比哈尔心律失常数据库（AR）上进行了测试。所提出的方法实现了78.58%的准确率，以及84.78%（LBBB）、51.23%（其他类型）和99.72%（RBBB）的灵敏度，优于基线方法。实验结果表明，对于具有记录级标签而非心跳标注的心电图数据集上的BBB分类，我们的方法将是一个不错的选择。
