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Abnormality classification from electrocardiograms with various lead combinations
Zhuoyang Xu1, Yangming Guo1, Tingting Zhao1
1Ping An Healthcare Technology, Beijing, People's Republic of China.
Insights
This study developed robust models for classifying 30 cardiac abnormalities from electrocardiogram (ECG) signals using various lead combinations. The framework achieved an average score of 0.58 in a challenge, demonstrating effective ECG analysis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Cardiovascular diseases are a leading cause of death, necessitating cost-effective and accurate early diagnosis of cardiac abnormalities.
- Developing generalized models for identifying multiple cardiac abnormalities from diverse electrocardiogram (ECG) datasets, including 12-lead and reduced-lead signals, presents significant challenges due to data divergence and label corruption.
Purpose of the Study:
- To build robust and accurate models capable of classifying 30 types of cardiac abnormalities from various combinations of ECG leads.
- To address data divergence, label corruption, and distribution uncertainty in ECG datasets for improved model generalization.
Main Methods:
- A preprocessing workflow was implemented to mitigate data divergence across different ECG datasets.
- A squeeze-and-excitation deep residual network was employed as the base model to capture lead-wise relationships in ECG signals.
- Novel strategies including cross-relabeling, sign-augmented loss, pos-if-any-pos ensemble, and dataset-wise cross-evaluation were utilized to handle corrupted labels and data distribution uncertainties.
Main Results:
- The proposed framework achieved challenge metric scores of 0.57, 0.59, 0.59, 0.58, and 0.57 for 12-, 6-, 4-, 3-, and 2-lead ECG signals, respectively.
- An averaged challenge metric score of 0.58 was obtained across all lead versions in the Physionet/Computing in Cardiology Challenge 2021.
- The models demonstrated accurate identification of 30 ECG abnormalities on hidden test data, validating the framework's effectiveness.
Conclusions:
- The developed models, trained on large datasets, accurately classify 30 ECG abnormalities using various lead combinations.
- The proposed framework effectively addresses challenges in ECG data, leading to robust models for cardiac abnormality detection.
- The performance on hidden test data confirms the significant potential of the proposed approaches for real-world clinical applications.
Abstract:
Objective. As cardiovascular diseases are a leading cause of death, early and accurate diagnosis of cardiac abnormalities for a lower cost becomes particularly important. Given electrocardiogram (ECG) datasets from multiple sources, there exist many challenges to the development of generalized models that can identify multiple types of cardiac abnormalities from both 12-lead ECG signals and reduced-lead ECG signals. In this study, our objective is to build robust models that can accurately classify 30 types of abnormalities from various lead combinations of ECG signals.Approach. Given the challenges of this problem, we propose a framework for building robust models for ECG signal classification. Firstly, a preprocessing workflow is adopted for each ECG dataset to mitigate the problem of data divergence. Secondly, to capture the lead-wise relations, we use a squeeze-and-excitation deep residual network as our base model. Thirdly, we propose a cross-relabeling strategy and apply the sign-augmented loss function to tackle the corrupted labels in the data. Furthermore, we utilize a pos-if-any-pos ensemble strategy and a dataset-wise cross-evaluation strategy to handle the uncertainty of the data distribution in the application.Main results. In the Physionet/Computing in Cardiology Challenge 2021, our approach achieved the challenge metric scores of 0.57, 0.59, 0.59, 0.58, 0.57 on 12-, 6-, 4-, 3- and 2-lead versions and an averaged challenge metric score of 0.58 over all the lead versions.Significance. Using the proposed framework, we have developed the models from several large datasets with sufficiently labeled abnormalities. Our models are able to identify 30 ECG abnormalities accurately based on various lead combinations of ECG signals. The performance on hidden test data demonstrates the effectiveness of the proposed approaches.
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