CNN‑BiLSTM Model Shows Optimal Balance for Multi‑Label ECG Classification

Global: CNN‑BiLSTM Model Shows Optimal Balance for Multi‑Label ECG Classification

In January 2026, researchers released a systematic study evaluating deep learning architectures for multi‑label electrocardiogram (ECG) classification, focusing on the PTB‑XL dataset that contains 23 diagnostic categories. The work, posted on arXiv, aimed to determine whether added architectural complexity improves predictive performance and clinical applicability.

Background

Accurate multi‑label ECG interpretation is challenged by simultaneous cardiac conditions, pronounced class imbalance, and long‑range temporal dependencies across multiple leads. Prior efforts have increasingly adopted deep and stacked recurrent networks, yet the necessity of such depth has not been rigorously quantified.

Methodology

The authors compared a morphology‑driven convolutional neural network (CNN) baseline with several recurrent extensions, including LSTM, GRU, bidirectional LSTM (BiLSTM), and their stacked variants. Recurrent layers were added sequentially to assess contributions to temporal modeling while monitoring model size and training stability.

Key Findings

Results indicated that a CNN combined with a single BiLSTM layer achieved the most favorable trade‑off between performance and complexity. This configuration recorded a Hamming loss of 0.0338, macro‑AUPRC of 0.4715, micro‑F1 score of 0.6979, and subset accuracy of 0.5723, surpassing deeper recurrent stacks on the same metrics.

Depth vs. Performance

While stacked recurrent models occasionally improved recall for rare diagnoses, they generally reduced precision and exhibited signs of overfitting, leading to diminishing returns as depth increased. The authors conclude that additional recurrent layers do not consistently translate into better generalization.

Clinical Implications

The study suggests that aligning model architecture with the intrinsic temporal structure of ECG signals—rather than maximizing depth—enhances robustness, a consideration that may streamline deployment in clinical decision‑support systems.

Conclusion

Overall, the comparative evaluation provides empirical evidence that a modest CNN‑BiLSTM architecture can deliver strong multi‑label ECG classification performance while maintaining manageable model complexity, informing future research and practical implementations.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.