ECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE

TL;DR

This work demonstrates 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.

Abstract

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

Figures (7)

• Figure 1: Confusion matrix for NORM class showing excellent recall (91%) with 877 true positives out of 964 actual normal cases, indicating the model's strong ability to correctly identify healthy ECGs.
• Figure 2: Confusion matrix for CD (Conduction Disturbances) class showing moderate performance with 348 true positives and 150 false negatives, reflecting the subtle ECG changes associated with conduction abnormalities.
• Figure 3: Confusion matrix for HYP (Hypertrophy) class revealing the model's primary weakness, with 131 false negatives (50% missed cases) out of 263 actual hypertrophy cases, highlighting the challenge of detecting this subtle condition.
• Figure 4: Confusion matrix for MI (Myocardial Infarction) class showing balanced performance with 375 true positives and 178 false negatives, indicating reasonable but improvable detection of acute cardiac events.
• Figure 5: Confusion matrix for STTC (ST/T-wave Changes) class demonstrating good recall with 408 true positives and relatively low false negatives (115), showing effective detection of ischemic changes.