用简单模型+数据优化，实现高精度心电图分类
ECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE
- 简化CNN-VAE结构，结合数据预处理和类别平衡
- 在PTB-XL数据集上达87.01%准确率，仅19.7万参数
- 适合医疗信号分析与小样本场景的模型设计参考
自动心电图（ECG）分类对心血管疾病早期发现至关重要。尽管近年方法多依赖复杂深度神经网络，本文表明，通过精心的数据预处理、类别平衡及简化的卷积神经网络与变分自编码器（CNN-VAE）结合，可在显著降低模型复杂度的同时取得竞争力性能。基于公开的PTB-XL数据集，我们在五个诊断类别（CD、HYP、MI、NORM、STTC）上实现了87.01%的二分类准确率和0.7454的加权F1分数，模型仅有197,093个可训练参数。研究强调数据驱动方法的重要性，指出系统性预处理与平衡策略对医学信号分类的关键作用。同时识别出少数类（尤其是肥厚）检测的挑战，并为未来不平衡心电图数据集处理提供改进方向。
原文摘要 · Abstract (English)
Automated electrocardiogram (ECG) classification is essential for early detection of cardiovascular diseases. While recent approaches have increasingly relied on deep neural networks with complex architectures, we demonstrate that careful data preprocessing, class balancing, and a simplified convolutional neural network combined with a variational autoencoder (CNN-VAE) architecture can achieve competitive performance with significantly reduced model complexity. Using the publicly available PTB XL dataset, we achieve 87.01% binary accuracy and 0.7454 weighted F1-score across five diagnostic classes (CD, HYP, MI, NORM, STTC) with only 197,093 trainable parameters. Our work emphasises the importance of data-centric machine learning practices over architectural complexity, demonstrating that systematic preprocessing and balanced training strategies are critical for medical signal classification. We identify challenges in minority class detection (particularly hypertrophy) and provide insights for future improvements in handling imbalanced ECG datasets. Index Terms: ECG classification, convolutional neural networks, class balancing, data preprocessing, variational autoencoders, PTB-XL dataset
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