Transfer learning application of a novel frequency shift convolutional neural network method for atrial fibrillation classification
Authors: Kent M , Vasconcelos L , Ansari S , Ghanbari H , Nenadic I 
Published: May 2022

Funding Acknowledgements

Type of funding sources: None.

Background

There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching &gt;85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain with cross-institutional algorithm evaluation. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution used to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. TL would thus require additional ECG signal processing which inherently introduces noise and has high computational costs. Our previous work has shown that frequency domain (FD) convolutional neural networks (CNN) outperform traditional time domain (TD) CNN methods in detecting atrial fibrillation (Afib) and are more robust against noise and sampling variations.

Purpose

In this study, we explore ECG transfer learning by comparing performance of TD and FD CNN ECG classification models. Examine the hypothesis that FD signal analysis can overcome limitations inherent to TD and demonstrate application for Afib classification in three different publicly available datasets.

Methods

PTB-XL ECG dataset was used to train TD and FD CNN models for Afib classification. TL on the two data sets, Lobachevsky University Electrocardiography Database (LUDB) and KURIAS-ECG database (KURIAS), was performed with portions of each dataset added to PTB-XL training data before test runs on the remaining ECGs. The models were also tested directly on the two datasets (cross-testing) for comparison.

Results

Results of TL are summarized in Fig. 1. Panels (a) and (b) show TD and FD performance on PTB-XL dataset, with FD outperforming TD. Panels (c) and (d) show TL of PTB-XL trained TD and FD models applied on LUDB Afib data with FD outperforming TD. Panels (e) and (f) show TL of PTB-XL trained models applied to KURIAS Afib ECGs, with FD again outperforming TD. Table 1 summarizes results of TL and direct cross-testing with TL outperforming cross-testing.

Conclusions

FD models were superior to TD models in Afib classification, both in cross-testing and TL. TL technique, in which a pre-trained model is used as starting point for novel dataset training, outperformed direct cross-testing. TL with FD CNN has the capacity to leverage models trained on a particular set of ECGs to classify ECGs with varying signal acquisition properties. FD CNN may be robust to inter-institutional variability and has potential for widespread application with no compromise to ECG classification power. A potential application of these findings is deployment of ECG classification CNN models trained at institutions with large ECG databases to hospitals with smaller datasets.