论文标题
通过心跳计数和人口统计数据集成，增强基于深度学习的3铅ECG分类
Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration
论文作者
论文摘要
如今，越来越多的人被诊断出患有心血管疾病（CVD），这是全球死亡的主要原因。识别这些心脏问题的金标准是通过心电图（ECG）。标准的12铅ECG广泛用于临床实践和大多数当前研究。但是，使用较少的线索可以使ECG更加普遍，因为它可以与便携式或可穿戴设备集成。本文介绍了两种新型技术，以提高当前深度学习系统的3铅ECG分类的性能，使其与使用标准12铅ECG训练的模型可比。具体而言，我们提出了一种以心跳回归数量的形式的多任务学习方案，以及将患者人口统计数据整合到系统中的有效机制。随着这两个进步，我们在两个大规模的ECG数据集（即Chapman和CPSC-2018）上获得了F1分数为0.9796和0.8140的分类性能，这些数据分别超过了当前最新的ECG分类方法，甚至超过了对12个测定数据训练的方法。为了鼓励进一步开发，我们的源代码可在https://github.com/lhkhiem28/lightx3ecg上公开获得。
Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). The standard 12-lead ECG is widely used in clinical practice and the majority of current research. However, using a lower number of leads can make ECG more pervasive as it can be integrated with portable or wearable devices. This article introduces two novel techniques to improve the performance of the current deep learning system for 3-lead ECG classification, making it comparable with models that are trained using standard 12-lead ECG. Specifically, we propose a multi-task learning scheme in the form of the number of heartbeats regression and an effective mechanism to integrate patient demographic data into the system. With these two advancements, we got classification performance in terms of F1 scores of 0.9796 and 0.8140 on two large-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, which surpassed current state-of-the-art ECG classification methods, even those trained on 12-lead data. To encourage further development, our source code is publicly available at https://github.com/lhkhiem28/LightX3ECG.
