Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation

Zhiguo Qu; Wenke Shi; Prayag Tiwari

doi:10.1016/j.compbiomed.2023.107549

Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation

Comput Biol Med. 2023 Sep 30:166:107549. doi: 10.1016/j.compbiomed.2023.107549. Online ahead of print.

Authors

Zhiguo Qu¹, Wenke Shi², Prayag Tiwari³

Affiliations

¹ Jiangsu Collaborative Innovation Center of Atmospheric Environment, the Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China; School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China. Electronic address: 002359@nuist.edu.cn.
² School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China. Electronic address: 20211221031@nuist.edu.cn.
³ School of Information Technology, Halmstad University, Sweden. Electronic address: prayag.tiwari@ieee.org.

PMID: 37839222
DOI: 10.1016/j.compbiomed.2023.107549

Abstract

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.

Keywords: Abnormal electrocardiogram; Data imbalance; Generative algorithm; Quantum generative adversarial network.