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Quantum-convolution-based hybrid neural network model for arrhythmia detection

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Abstract

This paper proposes a quantum convolutional hybrid neural network (QCHNN) model for cardiac arrhythmia detection that integrates quantum computing (QC) into the convolutional neural network (CNN). By leveraging the automatic feature extraction capability of the CNN and the entanglement property of QC, the robustness against noise is improved. QCHNN ultimately realizes a robust and highly accurate detection model. First, the model converts the electrocardiography (ECG) signal into two-dimensional gray-scale images, thereby mitigating the effects of signal noise. We then use an annular parameterized quantum circuit (PQC) to form a fully connected quantum (FCQ) layer, which enhances robustness effectively. In this quantum layer, the PQC not only provides sufficient entanglement to complete the feature fusion task, but also can control the number of parameters. Finally, we perform experimental simulations to verify the high accuracy and robustness of the proposed model. The results show that the detection accuracy of QCHNN can be improved by about 15.94% in a small sample, and the best accuracy achieved for five types of arrhythmias is 98.58%. Moreover, we prove that the QCHNN model is more tolerant of noise than the CNN model by testing four types of noise.

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Data availability

No datasets were generated or analyzed during the current study.

Abbreviations

ECG:

Electrocardiogram

QCHNN:

Quantum convolutional hybrid neural network

QC:

Quantum computer

QCs :

Quantum computers

CNN :

Convolutional neural network

ECG :

Electrocardiography

PQC :

Parameterized quantum circuit

FCQ :

Fully connected quantum

ML :

Machine learning

DL :

Deep learning

QML :

Quantum machine learning

NISQ :

Noisy intermediate-scale quantum

NNs :

Neural networks

FC :

Fully connected

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Funding

This work was supported in part by the National Natural Science Foundation of China (Grant no. 62103070), in part by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant no. KJZD-K202301103), and in part by the Chongqing Natural Science Foundation (Grant no. CSTB2023NSCQ-MSX0539).

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Authors and Affiliations

  1. College of Computer Science and Enginneering, Chongqing University of Technology, Chongqing, 40054, China

    Shiyue Zhang & Aijuan Wang

  2. Department of Computer Science, Old Dominion University, Norfolk, 23435, VA, USA

    Lusi Li

Authors
  1. Shiyue Zhang
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  2. Aijuan Wang
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  3. Lusi Li
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Contributions

Shiyue Zhang, Aijuan Wang, and Lusi Li wrote the main manuscript text, and Shiyue Zhang prepared all figures. All authors reviewed the manuscript. These authors contributed equally to this work.

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Correspondence to Aijuan Wang.

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Zhang, S., Wang, A. & Li, L. Quantum-convolution-based hybrid neural network model for arrhythmia detection. Quantum Mach. Intell. 6, 75 (2024). https://doi.org/10.1007/s42484-024-00207-7

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