论文标题
可穿戴的ECG监视器，用于深度学习的实时心血管疾病检测
A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection
论文作者
论文摘要
心血管疾病已成为危及人类生活和健康的最重要威胁之一。最近，心电图（ECG）监测已通过Holter监视转化为远程心脏监测。但是，广泛使用的抛光剂会给携带它们带来很大的不适和不便。我们在这项工作中开发了一个新的无线心电图补丁，并应用了基于卷积神经网络（CNN）和长期短期记忆（LSTM）模型的深度学习框架。但是，我们发现使用现有技术的模型无法区分我们新获得的数据集中两种主要的心跳类型（室外过早的节拍和房颤），从而导致58.0％的较低准确性。我们提出了一种半监督的方法，使用基于置信度的培训来处理标记不好的数据样本。实验结果得出的结论是，所提出的方法可以接近90.2％的平均精度，即比常规ECG分类方法的准确性高5.4％。
Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the individuals who carry them. We developed a new wireless ECG patch in this work and applied a deep learning framework based on the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models. However, we find that the models using the existing techniques are not able to differentiate two main heartbeat types (Supraventricular premature beat and Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy of 58.0 %. We proposed a semi-supervised method to process the badly labelled data samples with using the confidence-level-based training. The experiment results conclude that the proposed method can approach an average accuracy of 90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification methods.
