Key result
Handcrafted features rival DNNs for ECG age estimation while minimizing computational resources and providing explainability.
Why the study?
Neural networks for ECG age estimation have not been extensively evaluated regarding ECG acquisition parameters, and recent studies have discounted handcrafted features compared to deep neural networks.
Do fully connected networks trained with handcrafted features match the performance of end-to-end deep neural networks for ECG age estimation?
Do fully connected networks trained with handcrafted features match the performance of end-to-end deep neural networks for ECG age estimation?
Handcrafted ECG features offer a computationally efficient and explainable alternative to deep neural networks for estimating ECG age, a surrogate marker of cardiovascular well-being.
An electrocardiogram (ECG) is a signal that captures the electrical activity generated by the cardiac muscles during contraction and relaxation. Prior research has shown that the delta between ECG-derived age and chronological age is a general indicator of cardiovascular health and mortality (i.e., cardiovascular well-being). Increased delta age is strongly associated with cardiovascular conditions, such as increased vascular aging. Despite the clinical significance of ECG-derived age, neural networks for ECG age estimation have not been extensively evaluated with respect to ECG acquisition parameters. Furthermore, recent studies have discounted the use of handcrafted features compared to the deep neural network for the ECG age estimation task. To overcome these limitations, we conduct a comprehensive study determining the influence of ECG acquisition parameters on neural network performance. We compare the performance of deep neural networks with fully connected networks trained with state-of-the-art handcrafted features to showcase the utility and significance of ECG feature engineering. Overall, we demonstrate that handcrafted features can compete with end-to-end deep neural networks for ECG age estimation while utilizing minimal computational resources and providing explainability.Clinical relevance- ECG age is a simple and intuitive surrogate metric for cardiovascular well-being that is easily understood by both patients and doctors. An elevated ECG age signals potential cardiovascular decline, enabling it to serve as a tracking tool for monitoring improvements through medications, lifestyle changes, or surgical interventions. Additionally, determining optimal acquisition parameters could guide the development of ECG-based tools better suited for various hardware platforms, including mobile devices, IoT devices, and wearables. Furthermore, the handcrafted features analysis revealed key ECG parameters that are relevant for cardiovascular well-being.
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Ansari et al. (2025) studied Cardiovascular well-being. Handcrafted features for ECG age estimation vs. End-to-end deep neural networks was evaluated on ECG age estimation performance. Handcrafted features can compete with end-to-end deep neural networks for ECG age estimation while utilizing minimal computational resources and providing explainability.
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