Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs
Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs
复制标题
DOI：
10.23919/cinc53138.2021.9662757
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Long Chen;Zheheng Jiang;T. Almeida;F. Schlindwein;Jakevir S. Shoker;André Ng;Huiyu Zhou;Xin Li
中科院分区：
文献类型：
--
作者：
Long Chen;Zheheng Jiang;T. Almeida;F. Schlindwein;Jakevir S. Shoker;André Ng;Huiyu Zhou;Xin Li
心脏疾病的自动检测和分类在临床心电图（ECG）分析中起着至关重要的作用。深度学习方法对于自动特征提取是有效的，并且在ECG分类中显示出有希望的结果。在这项工作中，我们提出了一个深时空心电图网络（ST-ECGNet）提取强大的时空特征检测多个心脏疾病的多导联心电图数据。拟议的ST-ECGNet结合了用于提取局部空间特征的卷积神经网络（CNN）模块，用于捕获全局空间特征的注意力模块，以及用于从ECG数据中提取时间特征的双向门控递归单元（Bi-GRU）模块。具体来说，注意力机制使我们的深度学习架构能够专注于输入中最重要和最有用的部分，以做出更准确的预测。在2021年心脏病学生理网络/计算挑战赛中，我们的参赛作品没有在挑战赛的测试数据上进行正式排名和评分，因为我们的代码在正式阶段没有成功处理，并且在错误的情况下未能运行。
Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors.