论文标题
通过生成对抗网络进行分层深度学习，用于从ECG信号进行自动心脏诊断
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals
论文作者
论文摘要
心脏病是美国死亡的主要原因。准确的心脏病检测对于及时的医学治疗至关重要，以挽救患者的生命。常规使用心电图（ECG）是医生评估心脏电活动并检测可能异常心脏条件的最常见方法。充分利用ECG数据进行可靠的心脏病检测取决于开发有效的分析模型。在本文中，我们提出了一个具有生成对抗网络（GAN）的两级分层深度学习框架，用于自动诊断ECG信号。第一级模型由使用GAN（MADEGAN）的记忆启动的深度自动编码器组成，该模型旨在将异常信号与正常的ECG区分开以进行异常检测。第二级学习的目的是旨在强大的多级分类用于不同的心律失常识别，这是通过将转移学习技术整合到从一级学习中将知识与多支分支架构转移到多分支架构以处理数据含量和不平衡数据问题的方法来实现的。我们使用MIT-BIH心律失常数据库中的现实世界医学数据评估了拟议框架的性能。实验结果表明，我们提出的模型优于当前实践中通常使用的现有方法。
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is of critical importance for timely medical treatment to save patients' lives. Routine use of electrocardiogram (ECG) is the most common method for physicians to assess the electrical activities of the heart and detect possible abnormal cardiac conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diagnosis of ECG signals. The first-level model is composed of a Memory-Augmented Deep auto-Encoder with GAN (MadeGAN), which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmias identification, which is achieved by integrating the transfer learning technique to transfer knowledge from the first-level learning with the multi-branching architecture to handle the data-lacking and imbalanced data issue. We evaluate the performance of the proposed framework using real-world medical data from the MIT-BIH arrhythmia database. Experimental results show that our proposed model outperforms existing methods that are commonly used in current practice.
