Bashar Syed Khairul, Hossain Md-Billal, Lázaro Jesús, Ding Eric Y, Noh Yeonsik, Cho Chae Ho, McManus David D, Fitzgibbons Timothy P, Chon Ki H
Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut.
Aragon Institute for Engineering Research, University of Zaragoza, Zaragoza, Spain.
Cardiovasc Digit Health J. 2021 May 21;2(3):179-191. doi: 10.1016/j.cvdhj.2021.05.004. eCollection 2021 Jun.
Atrial fibrillation (AF) is the world's most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic.
To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device.
In our 2-step algorithm, we first calculate the R-R interval variability-based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects.
When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy.
Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.
心房颤动（AF）是全球最常见的心律失常，即使几分钟的房颤发作也会增加并发症风险，包括中风。然而，由于房颤可能是阵发性、短暂且无症状的，因此常常未被诊断出来。
为了促进更好的房颤监测，我们研究了使用从新型可穿戴臂带设备记录的连续心电图（ECG）信号检测房颤的可行性。
在我们的两步算法中，我们首先计算基于R-R间期变异性的特征，以捕捉可能表明一段数据包含房颤的随机性，随后将正常窦性心律与可能的房颤发作区分开来。接下来，我们使用密度庞加莱图衍生的图像域特征以及支持向量机，将房性/室性早搏发作与任何房颤发作区分开来。我们使用从包含30名受试者的MIMIC-III（重症监护医学信息库III）数据库子集中获得的ECG数据对我们的模型进行训练和验证。
当我们使用包含12名受试者的新型可穿戴臂带ECG数据集测试我们的模型时，所提出的方法的灵敏度、特异性、准确率和F1分数分别达到了99.89%、99.99%、99.98%和0.9989。此外，与使用臂带数据的几种现有方法相比，我们提出的方法表现更优，这显示了其有效性。
我们的研究表明，新型可穿戴臂带设备和我们的算法可以作为一种潜在工具，用于高精度的连续房颤监测。
