Abstract
Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model–based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.
Highlights
Cardiovascular disease has become the leading cause of death globally [1,2]
In response to the challenges stated above, we propose a category imbalance and cost-sensitive thresholding (CICST) method to efficiently compute the thresholds for the binary classifiers according to predefined misclassification costs
Our method consists of three parts, namely the pre-processing for data cleaning, the learning model based on a deep neural network (DNN), and the thresholding mechanism (i.e., CICST) to address the challenges of category imbalance and cost-sensitive learning
Summary
Cardiovascular disease has become the leading cause of death globally [1,2]. Electrocardiogram (ECG) monitoring is widely used for the screening and follow-up management of cardiovascular diseases. The rapid increase in ECG data has led to shortages in qualified physician resources. The technology required for automatic ECG analysis and abnormality detection is in demand to cope with the rapid growth in ECG monitoring data [3]. Considering the diversity of cardiac conditions and the complex correlations between them, the automatic detection of ECG abnormalities can be formed as a multi-label classification (MLC) problem, i.e., multiple different kinds of abnormalities may coexist in a single ECG recording. An ideal ECG classifier, should identify all of the abnormalities existing in a recording
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