Federated Learning for Automated ECG Classification
Abstract
Deep learning-based classification using 12-lead electrocardiogram (ECG) exams has been shown to learn and accurately recognize a wide range of heart conditions, such as atrial fibrillation (AF). There is a need for a decentralized, privacy-oriented approach to conduct model training for automated ECG classification without having to share sensitive patient data in a central location, and for ensuring compliance to data privacy regulations in present day healthcare. Federated learning comes forward as a model training scheme for privacy-oriented, collaborative learning for a wide range of learning tasks. However, its efficacy can be challenged by heterogeneous (non-IID) data, and with communication constraints such as clients dropping from update rounds. The CODE15% dataset by the Clinical Outcomes in Digital Electrocardiology (CODE) study from the Telehealth Network of Minas Gerais, Brazil, holds a large collection of labelled digital ECG exams obtained between 2010 and 2017. This thesis explores the training of an AF classifier residual neural network on a subset of the CODE15% dataset, in centralized to federated learning. Its performance is observed under IID and non-IID datasets with varying feature distributions by patient age. Simulations are done with different client participation ratios, and with 3 federated aggregation strategies. Obtained findings show that federated training can successfully match and improve the performance to centralized training of the AF…
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