Key result
Fed-CL model effectively predicts AF from ECGs while preserving data privacy.
Why the study?
Deep learning models for predicting AF using ECG signals face privacy concerns regarding sensitive patient data, creating a need to balance accurate prediction with robust user privacy.
Does a federated learning system combining CNN and LSTM (Fed-CL) accurately predict atrial fibrillation from ECG signals while preserving data privacy?
Does a federated learning system combining CNN and LSTM (Fed-CL) accurately predict atrial fibrillation from ECG signals while preserving data privacy?
The proposed Fed-CL model utilizes federated learning with CNN and LSTM to enable accurate, privacy-preserving detection of atrial fibrillation from ECG signals.
Supports privacy-preserving federated learning for AF detection from ECG; hypothesis-generating and requires prospective validation before clinical adoption.
Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.
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Alreshidi et al. (2024) studied Atrial Fibrillation (n=5,500). Fed-CL (Federated Learning with CNN-LSTM) vs. Traditional centralized machine learning models was evaluated on Prediction of Atrial Fibrillation. The Fed-CL model, integrating CNN and LSTM networks within a federated learning framework, effectively predicted atrial fibrillation from ECG signals while preserving data privacy.
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