Ramirez Elisa, Ruiperez-Campillo Samuel, Casado-Arroyo Ruben, Merino José Luis, Vogt Julia E, Castells Francisco, Millet José
ITACA Institute, Universitat Politècnica de València, Valencia, Spain.
Department of Computer Science, ETH Zürich, Zurich, Switzerland.
Front Physiol. 2024 Oct 7;15:1452829. doi: 10.3389/fphys.2024.1452829. eCollection 2024.
Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases.
We employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance.
Our findings indicate that optimizing input configurations through redundancy reduction techniques can enhance the performance of deep learning models in cardiovascular diagnostics, provided that the information is preserved and minimally distorted.
For the first time, this research has quantified the redundancy present in the input by validating various redundancy reduction techniques using a CNN. This discovery paves the way for advancing biomedical signal processing research, simplifying model complexity, and enhancing diagnostic performance in cardiovascular medicine within reduced lead systems, such as Holter monitors or wearables.
心血管疾病的准确诊断通常依赖于心电图（ECG）。由于心脏向量位于三维空间中，而标准心电图由从中导出的12个投影或导联组成，因此必然存在冗余信息。本研究旨在使用卷积神经网络（CNN）量化这种冗余及其对心血管疾病分类任务的影响。
我们运用信号理论和互信息引入了一种新的冗余度量，并探索了冗余增强和减少技术。这包括导联选择和变换，以评估对神经网络性能的影响。
我们的研究结果表明，通过冗余减少技术优化输入配置可以提高深度学习模型在心血管诊断中的性能，前提是信息得以保留且失真最小。
本研究首次通过使用CNN验证各种冗余减少技术，量化了输入中存在的冗余。这一发现为推进生物医学信号处理研究、简化模型复杂性以及提高诸如动态心电图监测仪或可穿戴设备等简化导联系统中心血管医学的诊断性能铺平了道路。
