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Application of Federated Learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals

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Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2023)

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Abstract

Artificial Intelligence-based (AI) analysis of large, curated medical datasets is promising for providing early detection, faster diagnosis, and more effective treatment using low-power Electrocardiography (ECG) monitoring devices information. However, accessing sensitive medical data from diverse sources is highly restricted since improper use, unsafe storage, or data leakage could violate a person’s privacy. This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG from 12-lead sensor arrays collected from six heterogeneous sources. We evaluated the capacity of the resulting models to achieve equivalent performance compared to state-of-the-art models trained in a Centralized Learning (CL) fashion. Moreover, we assessed the performance of our solution over Independent and Identical distributed (IID) and Non-IID federated data. Our methodology involves machine learning techniques based on Deep Neural Networks and Long-Short-Term Memory models. It has a robust data preprocessing pipeline with feature engineering, selection, and data balancing techniques. Our AI models demonstrated comparable performance to models trained using CL, IID, and Non-IID approaches. They showcased advantages in reduced complexity and faster training time, making them well-suited for cloud-edge architectures.

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Notes

  1. 1.

    https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

  2. 2.

    Remark that several different interconnection architectures are used in ECG technologies available in the market and studied in the relevant literature, such as wireless technologies like WI-FI or BLE, or wire technologies such as USB, or non-volatile memory formats. Such interconnections aspects are beyond the scope of this paper.

  3. 3.

    Note that in case the recording has a different sampling frequency, various algorithms exist in the relevant literature to change the sampling frequency to a lower or a higher one without affecting the accuracy [66].

  4. 4.

    Note that ECG recordings that are longer than the agreed length can be split into multiple ones without loss of generality.

  5. 5.

    Code for morphological features: https://github.com/physionetchallenges/python-classifier-2020/blob/master/get_12ECG_features.py.

  6. 6.

    Code for spectral features: https://github.com/onlyzdd/ecg-diagnosis/blob/dfa9033d5ae7be135db63ff567e66fdb2b86d76d/expert_features.py.

  7. 7.

    TEAM2’s code: https://github.com/ZhaoZhibin/Physionet2020model.

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Acknowledgements

This work was partially supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU and PNRR351 TECHNOPOLE - NEXT GEN EU Roma Technopole - Digital Transition, FP2 - Energy transition and digital transition in urban regeneration and construction.

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  1. Sapienza University, Rome, Italy

    Daniel Mauricio Jimenez Gutierrez, Hafiz Muuhammad Hassan, Lorella Landi, Andrea Vitaletti & Ioannis Chatzigiannakis

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  1. Daniel Mauricio Jimenez Gutierrez
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Correspondence to Daniel Mauricio Jimenez Gutierrez .

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    Ioannis Chatzigiannakis

  2. Ionian University, Corfu, Greece

    Ioannis Karydis

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Jimenez Gutierrez, D.M., Hassan, H.M., Landi, L., Vitaletti, A., Chatzigiannakis, I. (2024). Application of Federated Learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals. In: Chatzigiannakis, I., Karydis, I. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2023. Lecture Notes in Computer Science, vol 14053. Springer, Cham. https://doi.org/10.1007/978-3-031-49361-4_3

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