Bortolan Giovanni, Christov Ivaylo, Simova Iana
Institute of Neuroscience IN-CNR, Corso Stati Uniti 4, 35127 Padova, Italy.
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria.
Diagnostics (Basel). 2021 Sep 14;11(9):1678. doi: 10.3390/diagnostics11091678.
The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.
本研究的主要目标是基于经典的基于规则的方法和卷积深度学习架构，提出相对简单的心电图（ECG）信号自动诊断技术。验证任务是在2020年生理网/心脏病学计算挑战赛的框架内进行的，其中七个包含66361份12导联心电图记录的数据库被用于训练集、验证集和测试集。整个训练集中共考虑了24种不同的诊断类别。基于规则的方法使用为每个诊断标签定义的形态学和时频心电图描述符。这些规则是从心脏病专家的知识库或教科书中提取的，在第一阶段没有直接的学习过程，而在第二阶段测试了一种改进方法。深度学习方法同时考虑原始心电图和中位数心搏信号。这些数据通过连续小波变换分析进行处理，获得时频域表示，并生成特定图像（心电图小波图）。然后将这些图像用于基于GoogLeNet拓扑结构的卷积神经网络的训练，以进行心电图诊断分类。为了测试目的进行了交叉验证评估。在挑战赛期间，共有217个团队提交了1395种算法。我们算法的诊断准确率在基于规则的方法中产生了0.325的挑战赛验证分数（CPU时间=35分钟），在深度学习方法中产生了0.426的分数（CPU时间=1664分钟），这使得我们的团队在比赛中获得了第12名。
