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Rule-Based Method and Deep Learning Networks for Automatic Classification of ECG

机译：基于规则的ECG自动分类方法和深度学习网络

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The objective of the study is to explore the potentiality of combining a classical rule-based method with a Deep Learning method for automatic classification of ECG for participation in PhysicNet/Computing in Cardiology Challenge 2020. Six databases are considered for training set. They consist 43101 12 -leads ECG recording, lasting from 6 to 60 seconds considering 24 diagnostic classes. The rule-based method is using morphological and time-frequency ECG descriptors, characterizing each diagnostic labels. These rules have been extracted from the knowledge-base of a physician, with no direct learning procedure in the first phase, while a refinement have been tested in the second phase. The Deep Learning method consider both raw ECG signals and median beat signals. These data are processed by continuous wavelet transform analysis obtaining a time-frequency domain representation, with the generation of specific images. These images are used for training Convolutional Neural Networks for ECG diagnostic classification. Official result of the classification accuracy of the ECGs Test set of our team named `Gio_Ivo' produced a challenge validation score of 0.325 for the rule based method, and a 0.426 for the Deep learning methodology with GoogleNet, which was chosen for the final score, obtaining a full test score of 0.298, placing us 12th out of 41 in the official ranking.
机译：该研究的目的是探讨将基于古典规则的方法与ECG自动分类进行了深入学习方法的潜力，以便参与心脏病学挑战2020的物理网络/计算。六个数据库被考虑用于训练集。他们包括43101 12 - 向外集中记录，考虑24诊断类别持续6到60秒。基于规则的方法是使用形态学和时频ECG描述符，表征每个诊断标签。这些规则已从医生的知识库中提取，在第一阶段没有直接学习程序，而在第二阶段已经测试了细化。深度学习方法考虑原始的ECG信号和中值拍信号。通过生成特定图像的连续小波变换分析来处理这些数据，从而获得时间频域表示。这些图像用于培训卷积神经网络，用于心电图诊断分类。官方结果的ECGS测试集的分类准确性我们的团队命名为“GIO_IVO”为规则的方法产生了0.325的挑战验证得分，以及Googlenet的深度学习方法的0.426，被选为最终得分，获得完整的测试得分为0.298，让我们在官方排名中排名第121分。

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