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Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Yang Liu¹, Qince Li^1,2, Kuanquan Wang¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

|November 25, 2021

Summary

This summary is machine-generated.

This study introduces a new deep learning framework, Category Imbalance and Cost-Sensitive Thresholding (CICST), for multi-label electrocardiogram (ECG) classification. CICST effectively addresses category imbalance and cost sensitivity, improving diagnostic accuracy for cardiovascular diseases.

Keywords:

category correlations category imbalance deep neural network electrocardiogram multi-label classification

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Area of Science:

Cardiology
Artificial Intelligence
Machine Learning

Background:

Automatic electrocardiogram (ECG) classification aids cardiovascular disease management.
Multi-label ECG classification is complex due to diverse disease combinations and imbalanced data.
Previous models often overlook the cost-sensitive nature of ECG classification.

Purpose of the Study:

To develop a novel deep learning framework for multi-label ECG classification.
To incorporate category imbalance and cost-sensitivity into the classification process.
To improve the performance and practicality of ECG diagnostic models.

Main Methods:

Proposed a novel deep learning framework combining a residual convolutional network and class-wise attention.
Introduced a Category Imbalance and Cost-Sensitive Thresholding (CICST) method.
Evaluated the framework using a cost-sensitive metric on multiple realistic datasets.

Main Results:

CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in 5-fold cross-validation.
Outperformed existing thresholding methods like rank-based, proportion-based, and fixed thresholding.
Demonstrated improved performance and practicality in multi-label ECG classification.

Conclusions:

The proposed CICST method effectively handles category imbalance and cost information in multi-label ECG classification.
This approach enhances the accuracy and clinical utility of automated ECG analysis.
CICST represents a significant advancement in applying AI for cardiovascular diagnostics.