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Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation
Nanjing University Of Information Science And Technology, Nanjing, China.
Nanjing University Of Information Science And Technology, Nanjing, China.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2851-4260
2023 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 166, p. 1-13, article id 107549Article in journal (Refereed) Published
Abstract [en]

To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG. © 2023 The Author(s)

Place, publisher, year, edition, pages
Oxford: Elsevier, 2023. Vol. 166, p. 1-13, article id 107549
Keywords [en]
Abnormal electrocardiogram, Data imbalance, Generative algorithm, Quantum generative adversarial network
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-51853DOI: 10.1016/j.compbiomed.2023.107549PubMedID: 37839222Scopus ID: 2-s2.0-85173810551OAI: oai:DiVA.org:hh-51853DiVA, id: diva2:1810489
Note

This work was supported by the National Natural Science Foundation of China (No. 61373131 , 62071240 ), PAPD and CICAEET funds.

Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2023-11-08Bibliographically approved

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Tiwari, Prayag

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