Lai Dakun, Zhang Xinshu, Zhang Yifei, Bin Heyat Md Belal
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:4897-4900. doi: 10.1109/EMBC.2019.8856342.
Atrial Fibrillation (AF) is one of the arrhythmias that is common and serious in clinic. In this study, a novel method of AF classification with a convolutional neural network (CNN) was proposed, and particularly two cardiac rhythm features of R-R intervals and F-wave frequency spectrum were combined into the CNN for a good applicability of mobile application. Over 23 patients' ten-hours of Electrocardiogram (ECG) records were collected from the MIT-BIH database, and each of which was segmented into 10s-data fragments to train the designed CNN and evaluate the performance of the proposed method. Specifically, a total of 83,461 fragments were collected, 49,952 fragments of which are the normal fragments (type-N) and the others are the AF fragments. As results, the obtained average accuracy of the proposed method combining the two proposed features is 97.3%, which is shown a relative higher accuracy comparing with either that of the detection with the feature of R-R intervals (95.7%) or that with the feature of F-wave frequency spectrum (93.9%). Additionally, the sensitivity and the specificity of the present method are both of a high level of 97.4% and 97.2%, respectively. In conclusion, the CNN based approach by combining the R-R interval series and the F-wave frequency spectrum would be effectively to improve the performance of AF detection. Moreover, the proposed classification of AF with 10s-data fragments also could be potentially useful for a wearable real-time monitoring application for a pre-hospital screening of AF.
心房颤动（AF）是临床上常见且严重的心律失常之一。在本研究中，提出了一种使用卷积神经网络（CNN）进行AF分类的新方法，特别是将R-R间期和F波频谱这两个心律特征结合到CNN中，以实现移动应用的良好适用性。从麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）数据库收集了23名患者超过十小时的心电图（ECG）记录，并将每条记录分割成10秒的数据片段，用于训练设计的CNN并评估所提方法的性能。具体而言，总共收集了83461个片段，其中49952个片段为正常片段（N型），其余为AF片段。结果表明，结合所提两个特征的所提方法获得的平均准确率为97.3%，与仅使用R-R间期特征检测的准确率（95.7%）或仅使用F波频谱特征检测的准确率（93.9%）相比，显示出相对更高的准确率。此外，本方法的敏感性和特异性分别高达97.4%和97.2%。总之，结合R-R间期序列和F波频谱的基于CNN的方法将有效提高AF检测的性能。此外，所提的使用10秒数据片段对AF进行分类的方法对于可穿戴实时监测应用在院前AF筛查中也可能具有潜在用途。
