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Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Raza, Ali, Tran, Kim Phuc, Koehl, Ludovic, Li, Shujun (2022) Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI. Knowledge-Based Systems, 236 . Article Number 107763. ISSN 0950-7051. (doi:10.1016/j.knosys.2021.107763) (KAR id:92005)

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Official URL:
https://doi.org/10.1016/j.knosys.2021.107763

Abstract

Deep learning plays a vital role in classifying different arrhythmias using electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems.

In this paper, to address the above-mentioned challenges, we design a novel end-to-end framework in a federated setting for ECG-based healthcare using explainable artificial intelligence (XAI) and deep convolutional neural networks (CNN). The federated setting is used to solve challenges such as data availability and privacy concerns. Furthermore, the proposed framework effectively classifies different arrhythmias using an autoencoder and a classifier, both based on a CNN. Additionally, we propose an XAI-based module on top of the proposed classifier for interpretability of the classification results, which helps clinical practitioners to interpret the predictions of the classifier and to make quick and reliable decisions. The proposed framework was trained and tested using the baseline Massachusetts Institute of Technology - Boston's Beth Israel Hospital (MIT-BIH) Arrhythmia database. The trained classifier outperformed existing work by achieving accuracy up to 94.5% and 98.9% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation. We also propose a new communication cost reduction method to reduce the communication costs and to enhance the privacy of users' data in the federated setting. While the proposed framework was tested and validated for ECG classification, it is general enough to be extended to many other healthcare applications.

Item Type: Article
DOI/Identification number: 10.1016/j.knosys.2021.107763
Uncontrolled keywords: Electrocardiography (ECG), Deep learning, Explainable AI (XAI), Privacy, Security, Federated learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
R Medicine > R Medicine (General) > R858 Computer applications to medicine. Medical informatics. Medical information technology
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5102.9 Signal processing
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.P3 Pattern recognition systems
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7885 Computer engineering. Computer hardware
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
University-wide institutes > Institute of Cyber Security for Society
Funders: Organisations -1 not found.
Depositing User: Shujun Li
Date Deposited: 04 Dec 2021 10:50 UTC
Last Modified: 22 Feb 2022 11:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92005 (The current URI for this page, for reference purposes)
Raza, Ali: https://orcid.org/0000-0001-8326-8325
Koehl, Ludovic: https://orcid.org/0000-0002-3404-8462
Li, Shujun: https://orcid.org/0000-0001-5628-7328
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