Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation
This offers a privacy-preserving and interpretable solution for large-scale healthcare diagnostics, though it is incremental in applying federated learning to a known medical problem.
The study tackled the challenge of early atrial fibrillation detection by evaluating federated learning with neural networks on raw ECG data, achieving a 15% improvement in F1 score over local training with a best federated model F1 of 77%.
Early detection of atrial fibrillation (AFib) is challenging due to its asymptomatic and paroxysmal nature. However, advances in deep learning algorithms and the vast collection of electrocardiogram (ECG) data from devices such as the Internet of Things (IoT) hold great potential for the development of an effective solution. This study assesses the feasibility of training a neural network on a Federated Learning (FL) platform to detect AFib using raw ECG data. The performance of an advanced neural network is evaluated in centralized, local, and federated settings. The effects of different aggregation methods on model performance are investigated, and various normalization strategies are explored to address issues related to neural network federation. The results demonstrate that federated learning can significantly improve the accuracy of detection over local training. The best performing federated model achieved an F1 score of 77\%, improving performance by 15\% compared to the average performance of individually trained clients. This study emphasizes the promise of FL in medical diagnostics, offering a privacy-preserving and interpretable solution for large-scale healthcare applications.