Learning to scale: deriving data-driven scaling laws for ECG-optimized CNNs

With the rise of deep learning, automated electrocardiogram (ECG) classification has made huge improvements. Computer Vision (CV) and Natural Language Processing (NLP) are drivers of the deep learning revolution. While scaling model size has proven critical for performance in CV and NLP, medical data like ECGs exhibit fundamentally different properties. Despite this, scaling laws from CV/NLP are often applied directly to ECG tasks, leading to architecturally suboptimal models and prompting practitioners to default to smaller, simpler architectures that are easier to optimize.

In this work, we present a systematic framework for deriving scaling laws and architectural design principles of Convolutional Neural Networks (CNNs) for ECG classification. The aim is first to provide a framework to obtain model scaling laws, second to provide general scaling laws for CNNs for ECG classification, and finally to provide different CNN architectures for different computational budgets.

We propose a data-driven framework to derive ECG-specific scaling laws. Starting from a network design space encompassing CNN principles (kernel size, residual connections, inverted bottlenecks, depthwise convolutional blocks, squeeze-and-excitation blocks), we train 500 randomly sampled architectures. By analyzing performance distributions via Shapley additive explanations (SHAP values), we quantify the impact of each design choice and deduce optimal scaling laws.

We used an open source dataset consisting of 88,000 twelve-lead ECG recordings from six different sources. The ECG classification task contains 30 labels describing cardiac abnormalities and sinus rhythm.

Our framework provides three key insights for CNN architectures in ECG classification: First, systematic scaling of model depth and width contributes more significantly to performance than other architectural choices (Figure 1). Second, specific design elements - inverted bottlenecks and squeeze-and-excitation blocks - consistently improve classification accuracy. Third, across various computational budgets, the proposed models outperform state-of-the-art CNNs (Figure 2).

Our results demonstrate that architectural scaling laws tailored to ECG classification lead to more effective and efficient CNNs than direct adaptations of CV models. This marks a paradigm shift from blindly adapting CV architectures to embracing a data-driven, ECG-specific design approach that systematically uncovers optimal model configurations for computational budget constraints. While our framework is validated on a large multi-source dataset, its generalizability to rare arrhythmias or low-data settings remains to be tested. Additionally, future work could evaluate hybrid or attention-based models (e.g., Transformers) to further advance ECG classification.

SHAP values for model performance