Decorrelative Network Architecture for Robust Electrocardiogram Classification

TL;DR

This paper introduces a decorrelation and Fourier partitioning-based ensemble method to improve the robustness and uncertainty estimation of ECG classification models, outperforming adversarial training in efficiency and resilience.

Contribution

A novel ensemble approach combining feature decorrelation and Fourier partitioning enhances robustness and uncertainty estimation in ECG classification without high computational costs.

Findings

Maintains accuracy on unperturbed data

Demonstrates superior robustness against adversarial attacks

Requires less computational overhead than adversarial training

Abstract

Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. As it is not possible to train networks that are accurate in all scenarios, models must recognize situations where they cannot operate confidently. Bayesian deep learning methods sample the model parameter space to estimate uncertainty, but these parameters are often subject to the same vulnerabilities, which can be exploited by adversarial attacks. We propose a novel ensemble approach based on feature decorrelation and Fourier partitioning for teaching networks diverse complementary features, reducing the chance of perturbation-based fooling. We test our approach on single and multi-channel electrocardiogram classification, and adapt adversarial training and DVERGE into the Bayesian ensemble framework for comparison. Our results indicate that the combination of decorrelation and Fourier partitioning generally maintains performance on unperturbed data while demonstrating superior robustness and uncertainty estimation on projected gradient descent and smooth adversarial attacks of various magnitudes. Furthermore, our approach does not require expensive optimization with adversarial samples, adding much less compute to the training process than adversarial training or DVERGE. These methods can be applied to other tasks for more robust and trustworthy models.