重新审视处理心脏病分类任务中的不平衡问题
Revisiting the Disequilibrium Issues in Tackling Heart Disease Classification Tasks
AI总结：
针对心脏病ECG数据集不平衡和高维导联信号导致过拟合的问题，提出通道幅度均衡器（CME）和倒置权重对数损失（IWL）两种方法，有效提升SOTA模型准确率5%至10%。
摘要：
在心脏病分类领域，存在两个主要障碍。首先，现有的心电图（ECG）数据集在不同模态间持续表现出不平衡和偏差。其次，这些时间序列数据由多种导联信号组成，导致卷积神经网络（CNN）对功率较高的信号过拟合，从而降低了深度学习（DL）过程的性能。此外，面对不平衡数据集时，此类高维数据的性能可能容易过拟合。当前的努力主要集中在通过设计新架构来增强DL模型，尽管存在这些明显挑战，却似乎忽视了核心问题，因此阻碍了心脏病分类的进展。为了解决这些障碍，我们提出的方法引入了两种简单直接的方法来增强分类任务。针对高维问题，我们在信号编码图像上采用通道幅度均衡器（CME）。该方法减少了特征数据范围的冗余，突出了数据集中的变化。同时，为抵消数据不平衡，我们提出倒置权重对数损失（IWL）以缓解数据间的不平衡。应用IWL损失时，最先进模型（SOTA）在CPSC2018数据集上的准确率提高达5%。CME与IWL结合也使其他基线模型的分类结果提升了5%至10%。
英文摘要：
In the field of heart disease classification, two primary obstacles arise. Firstly, existing Electrocardiogram (ECG) datasets consistently demonstrate imbalances and biases across various modalities. Secondly, these time-series data consist of diverse lead signals, causing Convolutional Neural Networks (CNNs) to become overfitting to the one with higher power, hence diminishing the performance of the Deep Learning (DL) process. In addition, when facing an imbalanced dataset, performance from such high-dimensional data may be susceptible to overfitting. Current efforts predominantly focus on enhancing DL models by designing novel architectures, despite these evident challenges, seemingly overlooking the core issues, therefore hindering advancements in heart disease classification. To address these obstacles, our proposed approach introduces two straightforward and direct methods to enhance the classification tasks. To address the high dimensionality issue, we employ a Channel-wise Magnitude Equalizer (CME) on signal-encoded images. This approach reduces redundancy in the feature data range, highlighting changes in the dataset. Simultaneously, to counteract data imbalance, we propose the Inverted Weight Logarithmic Loss (IWL) to alleviate imbalances among the data. When applying IWL loss, the accuracy of state-of-the-art models (SOTA) increases up to 5% in the CPSC2018 dataset. CME in combination with IWL also surpasses the classification results of other baseline models from 5% to 10%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
