Multi-Label Diagnosis of Arrhythmias Based on a Modified Two-Category Cross-Entropy Loss Function

Abstract

The 12-lead resting electrocardiogram (ECG) is commonly used in hospitals to assess heart health. The ECG can reflect a variety of cardiac abnormalities, requiring multi-label classification. However, the diagnosis results in previous studies have been imprecise. For example, in some previous studies, some cardiac abnormalities that cannot coexist often appeared in the diagnostic results. In this work, we explore how to realize the effective multi-label diagnosis of ECG signals and prevent the prediction of cardiac arrhythmias that cannot coexist. In this work, a multi-label classification method based on a convolutional neural network (CNN), long short-term memory (LSTM), and an attention mechanism is presented for the multi-label diagnosis of cardiac arrhythmia using resting ECGs. In addition, this work proposes a modified two-category cross-entropy loss function by introducing a regularization term to avoid the existence of arrhythmias that cannot coexist. The effectiveness of the modified cross-entropy loss function is validated using a 12-lead resting ECG database collected by our team. Using traditional and modified cross-entropy loss functions, three deep learning methods are employed to classify six types of ECG signals. Experimental results show the modified cross-entropy loss function greatly reduces the number of non-coexisting label pairs while maintaining prediction accuracy. Deep learning methods are effective in the multi-label diagnosis of ECG signals, and diagnostic efficiency can be improved by using the modified cross-entropy loss function. In addition, the modified cross-entropy loss function helps prevent diagnostic models from outputting two arrhythmias that cannot coexist, further reducing the false positive rate of non-coexisting arrhythmic diseases, thereby demonstrating the potential value of the modified loss function in clinical applications.

A Fault Diagnosis Method for Analog Circuits Based on Improved TQWT and Inception Model

“…When dealing with multi-classification problems, the objective function can be modified to combine the parameter-solving problems of multiple classification surfaces with each other and convert them into the optimization of one problem. A multi-classifier can also be constructed by combining multiple binary classifiers, commonly known as one-to-many methods, one-to-one methods, and so on [36,37]. The use of an SVM for the classification of the feature data of analog circuits has the characteristics of good general performance and adaptability.…”