用小波变换融合特征，解决心电图分类中的数据不平衡和噪声问题。
Enhancing Imbalanced Electrocardiogram Classification: A Novel Approach Integrating Data Augmentation through Wavelet Transform and Interclass Fusion
- 基于小波变换的跨类特征融合生成新训练数据
- 在CPSC 2018数据集上达到92%~99%的分类准确率
- 特别适合处理罕见心律失常的少样本分类任务
心电图（ECG）数据不平衡严重影响深度学习模型在心血管诊断自动化中的性能与鲁棒性，尤其表现为某些罕见心脏疾病样本严重不足。尽管可通过算法生成或过采样缓解类别偏斜，但其有效性尚未达成共识。此外，采集过程引入的噪声进一步增加分析难度。本文提出一种新型方法，结合小波变换的跨类特征融合技术，构建训练与测试集的特征库，并将原始数据与其对应特征库融合，实现更均衡的数据分布。在CPSC 2018数据集上，模型对Normal、AF、I-AVB、LBBB、RBBB、PAC、PVC、STD和STE等类别识别准确率分别达99%、98%、97%、98%、96%、92%、93%、92%和98%，平均准确率介于92%至98%之间。该方法在分类精度上超越现有所有已知算法。
原文摘要 · Abstract (English)
Imbalanced electrocardiogram (ECG) data hampers the efficacy and resilience of algorithms in the automated processing and interpretation of cardiovascular diagnostic information, which in turn impedes deep learning-based ECG classification. Notably, certain cardiac conditions that are infrequently encountered are disproportionately underrepresented in these datasets. Although algorithmic generation and oversampling of specific ECG signal types can mitigate class skew, there is a lack of consensus regarding the effectiveness of such techniques in ECG classification. Furthermore, the methodologies and scenarios of ECG acquisition introduce noise, further complicating the processing of ECG data. This paper presents a significantly enhanced ECG classifier that simultaneously addresses both class imbalance and noise-related challenges in ECG analysis, as observed in the CPSC 2018 dataset. Specifically, we propose the application of feature fusion based on the wavelet transform, with a focus on wavelet transform-based interclass fusion, to generate the training feature library and the test set feature library. Subsequently, the original training and test data are amalgamated with their respective feature databases, resulting in more balanced training and test datasets. Employing this approach, our ECG model achieves recognition accuracies of up to 99%, 98%, 97%, 98%, 96%, 92%, and 93% for Normal, AF, I-AVB, LBBB, RBBB, PAC, PVC, STD, and STE, respectively. Furthermore, the average recognition accuracy for these categories ranges between 92\% and 98\%. Notably, our proposed data fusion methodology surpasses any known algorithms in terms of ECG classification accuracy in the CPSC 2018 dataset.
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