Wang Zekai, Stavrakis Stavros, Yao Bing
Department of Industrial & Systems Engineering, The University of Tennessee, Knoxville, TN, 37996, USA.
University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
Comput Biol Med. 2023 Mar;155:106641. doi: 10.1016/j.compbiomed.2023.106641. Epub 2023 Feb 8.
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess the cardiac electrical activities and detect possible abnormal 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 ECG signal analysis. The first-level model is composed of a Memory-Augmented Deep AutoEncoder 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 arrhythmia 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 issues. We evaluate the performance of the proposed framework using real-world ECG data from the MIT-BIH arrhythmia database. Experimental results show that our proposed model outperforms existing methods that are commonly used in current practice.
心脏病是美国的主要死因。准确检测心脏病对于及时进行医疗救治以挽救患者生命至关重要。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情况的最常用方法。充分利用心电图数据进行可靠的心脏病检测依赖于开发有效的分析模型。在本文中，我们提出了一种带有生成对抗网络（GAN）的两级分层深度学习框架用于心电图信号分析。第一级模型由带有GAN的记忆增强深度自动编码器（MadeGAN）组成，其目的是从正常心电图中区分出异常信号以进行异常检测。第二级学习旨在针对不同心律失常识别进行稳健的多类分类，这是通过集成迁移学习技术来实现的，即将第一级学习中的知识转移到具有多分支架构的模型中，以处理数据缺乏和数据不平衡问题。我们使用来自麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心律失常数据库的真实世界心电图数据评估了所提出框架的性能。实验结果表明，我们提出的模型优于当前实践中常用的现有方法。
