Advancing DoA assessment through federated learning: A one-shot pseudo data approach

Abstract

Accurately measuring the Depth of Anaesthesia (DoA) during surgical procedures is crucial for patient safety. A significant challenge in developing effective machine learning models for DoA assessment is the lack of data from single organisations and preserving data privacy between institutions. Federated learning offers a solution by enabling multiple parties to collaboratively train models without exchanging data. However, traditional federated learning algorithms perform poorly in data heterogeneous, non-identically distributed data distribution scenarios. To address these challenges, we propose a one-shot federated learning framework, DoAFedP-NN, which facilitates federated learning with heterogeneous model development. The framework is tested in a range of model and data heterogeneity environments. This method enables the training of a global DoA prediction model across different medical facilities without sharing local data. The DoAFedP-NN model, utilising neural network design with entropy and spectral feature extraction, is compared to benchmark federated learning architectures, demonstrating its advantage in handling heterogeneous medical data. Experimental results show that DoAFedP-NN achieves robust DoA estimation when compared to the Bispectral (BIS) index, with high correlation coefficients of 0.8472 and 0.8542 across independent databases. The proposed model outperforms locally developed models, showing significant improvements when validated against external datasets from different medical facilities. This paper makes the key contributions: (1) introduces a one-shot pseudo-data method for federated learning; (2) demonstrates the effectiveness of this approach for EEG-based DoA using real-world databases; (3) showcases the model’s ability to achieve high correlation with the BIS index while preserving patient privacy in a range of client distribution scenarios and under cross-validation. • This study introduces a novel federated learning framework, DoAFedP-NN, which utilises neural network architecture to improve the accuracy of EEG-based Depth of Anaesthesia (DoA) monitoring. By integrating data across multiple databases without sharing local patient data, the framework respects patient privacy while enhancing model performance. • The DoAFedP-NN model, employing entropy and spectral analysis, demonstrated robust DoA estimation capabilities, achieving high correlation coefficients across independent databases. • The research utilises a novel pseudo-data federated learning aggregation method to enable heterogeneous model development through one-shot a novel federated learning framework for EEG-based DoA analysis. The DoAFedP-NN model’s superior performance compared to locally trained models and its comparability to a traditional full aggregation model demonstrate the value of federated learning in achieving high analytical precision without compromising patient privacy.

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