ECG-QGAN: A ECG Generative Information System Based on Quantum Generative Adversarial Networks

According to statistics, the number of people suffering from cardiovascular diseases in China is about 330 million, and the number of deaths caused by cardiovascular diseases accounts for 40% of the total number of deaths each year. Under this circumstance, the development of heart disease assisted diagnosis systems is particularly important, but its development is limited by the lack of a large amount of electrocardiogram (ECG) clinical data that does not contain patient privacy information and needs to be annotated by medical experts. As an emerging discipline, quantum computing can explore larger and more complex state spaces by utilizing quantum superposition and entanglement properties, which is beneficial for generating high-quality and diverse electrocardiogram data similar to real clinical data. Therefore, we propose an electrocardiogram generative information system based on quantum generative adversarial networks, abbreviated as ECG-QGAN. The quantum generative adversarial network consists of a quantum bidirectional gated recurrent unit (QBiGRU) and a quantum convolutional neural network (QCNN). The system utilizes the entanglement property of quantum to improve the generative capability to produce ECG data that is consistent with the existing clinical data so that the heartbeat characteristics of cardiac patients can be preserved. The generator and discriminator of this system use QBiGRU and QCNN, respectively. The variational quantum circuit (VQC) designed based on matrix product state (MPS) and tree tensor network (TTN) is adopted, which enables the system to capture ECG data information more efficiently and generate qualified ECG data with fewer quantum resources. In addition, the system applies quantum Dropout technology to avoid overfitting issues during the training process. Finally, the experimental results show that the ECGs generated by ECG-QGAN have a higher average classification accuracy compared to other models for generating ECGs. It is also friendly to the current noisy intermediate scale quantum (NISQ) computers in terms of the number of quantum bits and circuit depth.