发表机构
Yildiz Technical University; Robert College（伊斯坦布尔技术大学; 罗伯特学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在PTB-XL数据集上比较五种深度学习模型及三种集成方法进行多标签12导联心电图分类，发现堆叠集成方法取得最佳宏观性能（AUROC 0.936），表明异构集成能进一步提升分类效果。
AI 中文摘要
本研究旨在比较不同深度学习架构和异构集成学习方法在PTB-XL数据集上进行多标签12导联心电图分类的性能。评估了五种不同的模型，即1D-ResNet18、双向Mamba、xLSTM、CWT-ViT-KAN和预训练的ECGFounder。利用PTB-XL数据集推荐的划分结构，将第1-8折分配为训练集，第9折为验证集，第10折为独立测试集。除单个模型外，还研究了三种不同的集成方法：等权软投票、基于验证集加权的软投票和堆叠。在单个模型中，ECGFounder取得了最高性能，其Macro AUROC为0.930，Macro AUPRC为0.823。对于集成模型，堆叠方法在主要宏观性能指标上取得了最高值，其Macro AUROC为0.936，Macro AUPRC为0.836，Macro F1为0.763。使用基于验证集加权的软投票方法取得了最高的子集准确率0.630。研究结果表明，结合不同表示学习方法的异构集成模型，在多标签心电图分类中能够比单个模型提供额外的性能提升。
英文摘要
This study aimed to compare the performance of different deep learning architectures and heterogeneous ensemble learning approaches for multi-label 12-lead ECG classification on the PTB-XL dataset. Five different models, namely 1D-ResNet18, Bidirectional Mamba, xLSTM, CWT-ViT-KAN, and the pre-trained ECGFounder, were evaluated. Utilizing the recommended split structure of the PTB-XL dataset, folds 1-8 were allocated as the training set, fold 9 as the validation set, and fold 10 as the independent test set. In addition to the individual models, three different ensemble approaches were investigated: Equal-Weight Soft Voting, Validation-Weighted Soft Voting, and stacking. Among the individual models, the highest performance was achieved by ECGFounder, with a Macro AUROC of 0.930 and a Macro AUPRC of 0.823. For the ensemble models, the highest values in the primary macro performance metrics were obtained by the stacking approach, achieving a Macro AUROC of 0.936, a Macro AUPRC of 0.836, and a Macro F1 of 0.763. The highest subset accuracy of 0.630 was achieved using the Validation-Weighted Soft Voting method. The findings indicate that heterogeneous ensemble models, which combine different representation learning approaches, can provide additional performance improvements over individual models in multi-label ECG classification.
Comments6 pages, 2 figures, 1 table
