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.
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.
The development of effective deep learning systems for cardiac disease diagnosis requires extensive and diverse electrocardiogram (ECG) databases, which remain scarce due to patient confidentiality regulations and costly medical annotation procedures. This paper introduces a novel hybrid Quantum Generative Adversarial Network (QGAN) that generates realistic 12-lead ECG signals trained on the MIT-BIH Arrhythmia Database. A parameterised quantum circuit (PQC) serves as the generative core, exploiting quantum superposition and entanglement to model biomedical signal distributions that traditional generators cannot efficiently replicate. The system pairs a PennyLane-based quantum generator with a 1D Convolutional Neural Network discriminator implemented in PyTorch, trained via the parameter-shift rule. Experimental results demonstrate that the Quantum PQC achieves a Fréchet Distance of 12.3 ±2.1, outperforming Classical CNN (15.8 ±3.2) and Classical LSTM (18.4 ±4.1) baselines while using fewer parameters. Generated 12-lead traces accurately reproduce P-waves, QRS complexes, T-waves, and inter-lead spatial relationships, validating quantum-augmented synthesis as a viable, privacy-preserving approach for cardiovascular AI training.
The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.
Although cardiovascular disease constitutes the primary cause of death in China, the clinical deployment of artificial intelligence-assisted diagnostic tools remains constrained by the limited availability of large-scale, high-fidelity electrocardiogram datasets with adequate privacy safeguards. Conventional generative adversarial networks (GANs) encounter persistent constraints regarding temporal sequence representation, elevated-dimensional attribute extraction, and optimization stability, concurrently failing to accommodate resource limitations inherent to contemporary noisy intermediate-scale quantum (NISQ) apparatus. Drawing inspiration from ensemble methodologies typified by random forests, this investigation introduces an ECG generative information architecture designated QRFGAN-ECG, predicated upon quantum random forest generative adversarial networks. The generative component implements quantum residual recurrent networks (QResRNN) to encapsulate extended-range cardiac cycle dependencies; conversely, the discriminative module incorporates quantum random forest convolutional networks (QRF-CNN) to execute multi-resolution attribute voting, thereby augmenting discrimination robustness. Variational quantum circuit architectures formulated through matrix product states (MPS) and tree tensor networks (TTN) substantially diminish qubit requirements and circuit depth. Furthermore, this study pioneerly integrates quantum dropout mechanisms with forest ensemble strategies to mitigate quantum overfitting phenomena. Empirical evaluations conducted across four publicly accessible ECG repositories demonstrate QRFGAN-ECG superiority over classical Against BiGRU-CNN, ECG-QGAN, and DCGAN baselines, the proposed framework demonstrates superior performance across Pearson correlation coefficient, percentage root-mean-square difference, root-mean-square error, and downstream classification accuracy. Adversarial training convergence improves by roughly one-third, with markedly enhanced robustness to four dominant quantum noise regimes. The generated ECG samples carry intrinsic labels, eliminating manual annotation overhead while precluding privacy exposure. These synthetic specimens readily support training set expansion, establishing a dependable data foundation for cardiac disease diagnostic assistance systems.
Cardiovascular diseases are currently the biggest cause of global death. A high portion of global mortality and expenditure on health are caused by such diseases, therefore, detecting abnormalities earlier and more correctly in cardiac conditions is essential for timely treatment. Diagnosis of the condition in communities or at clinical screening facilities uses techniques of electrocardiogram (ECG) and phonocardiogram (PCG). Nevertheless, actual patient assessment is hampered by the brief events, inter-patient variation, high levels of noise and erratic observation conditions. Today, deep learning techniques are improving automated screening of patients with potential cardiovascular conditions, but these methods achieve improved recognition results at the cost of larger model architecture, much greater processing load and significant susceptibility to variations in the quality and recording conditions of medical sensor recordings. Given these trade-offs, this research presents a set of quantum hybrid algorithms that use variational quantum circuits to carry out dense yet descriptive features for enhancing discriminability and stability in diagnosing cardiovascular disease. The research study focuses on three main challenges of detecting cardiovascular disease. First, arrhythmic events are often intermittent and subtle, making prompt ECG interpretation difficult, especially when input quality or resolution varies across devices and transmission conditions. Second, PCG recordings are vulnerable to noise and variability during recording, which can hinder generalisation in real-world screening settings. Third, ECG and PCG provide complementary electrical and mechanical insights into cardiac function. However, many automated methods analyse them separately or use traditional fusion strategies that do not fully leverage cross-modal interactions. To address these issues, the study presents three modular hybrid quantum algorithms that follow a consistent design philosophy and reusable components, facilitating the transition from unimodal classification to multimodal decision-making for cardiovascular disease detection. The first hybrid quantum algorithm for arrhythmia detection classifies multiple arrhythmia types by analysing their corresponding images. ECG readings are input to a pre-trained ResNet50 encoding model and are passed to the quantum classifier with a 10-qubit variational quantum circuit, simulated by PennyLane’s lightning.kokkos. Each image embedding then passes through a quantum convolutional layer before the final classification output is derived. Resolution of the input ECG signal is treated as an adjustable hyperparameter rather than a fixed input scale. The algorithm was tested on 123,998 images at 224x224 size drawn from the MIT-BIH Arrhythmia and PTB datasets. Performance was evaluated by setting test set resolutions between 80px and 320px. The algorithm consistently performed well at each resolution, with negligible calibration deviation and peak accuracy around 160px, offering practical trade-offs for its implementation. The algorithm reached a 99.98% prediction accuracy within 4.5 training hours. The integration of a variational quantum layer with a robust vision backbone shows that the algorithm is capable of performing direct ECG image classification and ensuring consistent performance across multiple resolutions, effectively rendering resolution as a flexible variable controllable by the end-user, not just a static fixed dimension. The second hybrid quantum algorithm classifies PCG signals for cardiovascular disease screening. PCG recordings derived from the PhysioNet/CinC 2016 dataset were transformed into Mel-spectrograms and fed into a ResNet50 feature extraction architecture linked with a 10-qubit variational quantum circuit executed using lightning.kokkos. In addition, Gaussian noise was added to verify the robustness of the presented algorithm against different levels of acoustic clutter typically encountered in a real clinical setting. This methodology resulted in significant outcomes, achieving 99.34% accuracy, 99.33% sensitivity and 99.35% specificity for clear PCG signals. The achieved performance was comparable with, or higher than, several previously reported PCG classification studies, although direct comparison is limited by differences in datasets and evaluation protocols.. In the presence of noisy PCG data, the algorithm also exhibited robust performance, reaching 96.68% accuracy at 20 dB SNR and 80.73% accuracy at 10 dB SNR, indicating tolerance to mild and moderate synthetic noise. This hybrid quantum machine learning approach innovates by combining a variational quantum circuit with spectrogram analysis and evaluating the tolerance of the algorithm to noise, suggesting its potential to maintain high accuracy despite realistic environmental interference. The third algorithm uses a Hybrid Quantum Fusion Network (HQFN) to combine ECG and PCG classifier-output representations through a variational quantum fusion layer. The ECG inputs were six-dimensional output-logit vectors derived from the MIT-BIH Arrhythmia and PTB Diagnostic ECG datasets, while the PCG inputs were two-dimensional outputlogit vectors derived from the PhysioNet/CinC 2016 dataset. Because the ECG and PCG data were obtained independently and were neither synchronised nor patient-matched, the experiment was framed as proof-of-concept class-consistent output-level fusion rather than patient-level multimodal diagnosis. Normal ECG outputs were paired with Normal PCG outputs, while Abnormal ECG outputs were paired with Abnormal PCG outputs. The source representations were split before pairing, and SMOTE was applied only to the training representation space. The combined eight-dimensional input was processed by an eight-qubit variational quantum circuit and mapped to a binary Normal/Abnormal decision through a classical output layer. On the held-out test set, the quantum fusion algorithm achieved 99.50% accuracy, with macro-averaged precision, recall and F1-score also at 99.50%, specificity of 99.01% and ROC–AUC of 0.999994. However, a compact classical fusion baseline achieved 100% accuracy under the same experimental conditions. Therefore, the results do not demonstrate a quantum performance advantage. Instead, they establish the feasibility of applying a variational quantum circuit to compact ECG and PCG classifier outputs within a controlled output-level fusion framework. Future work should evaluate patient-matched and synchronised recordings, richer intermediate representations, discordant modality pairs and parameter-matched classical baselines. This study presents three scalable modular hybrid quantum algorithms tailored for three methods: the use of ECG only, the use of PCG only and a hybrid method that combines both ECG and PCG signals to improve the accuracy of the detection of cardiovascular diseases. Results show that quantum machine learning could potentially generate highly accurate values about the classification of both arrhythmias and heart sounds, even with issues that commonly exist in real-world scenarios, such as lower resolution inputs and added noise. Moreover, the presented algorithms are intended to function as versatile reusable blocks capable of expanding from single modality detection up to the fusion of different cardiovascular signals through quantum computation, paving the way for the utilisation of quantum machine learning in future cardiovascular disease screening.
Electrocardiography (ECG) is a primary diagnostic tool for identifying cardiac arrhythmias, a critical indicator of heart disease. While ECG signals contain vital information about the patient’s health conditions, their high-resolution waveform nature poses significant challenges in effective feature extraction. Recent advancements in quantum computing provide a transformative opportunity to accelerate research in data-driven predictive modeling for healthcare. This paper investigates a variety of Quantum Convolutional Neural Network (QCNN) architectures for ECG signal classification. By leveraging optimized convolutional structures, adaptable pooling layers, and advanced encoding techniques, our QCNN model is designed to preserve essential ECG characteristics while addressing challenges in dimensionality reduction. Experimental results demonstrate that our approach holds significant potential for enhanced arrhythmia classification. This innovative QCNN model has the potential to advance ECG-based diagnostics for heart disease, offering valuable support for clinical decision-making in cardiac care.
Early identification of abnormal physiological patterns is essential for the timely detection of cardiac disease. This work introduces a hybrid quantum-classical convolutional neural network (QCNN) designed to classify S3 and murmur abnormalities in heart sound signals. The approach transforms one-dimensional phonocardiogram (PCG) signals into compact two-dimensional images through a combination of wavelet feature extraction and adaptive threshold compression methods. We compress the cardiac-sound patterns into an 8-pixel image so that only 8 qubits are needed for the quantum stage. Preliminary results on the HLS-CMDS dataset demonstrate 93.33% classification accuracy on the test set and 97.14% on the train set, suggesting that quantum models can efficiently capture temporal-spectral correlations in biomedical signals. To our knowledge, this is the first application of a QCNN algorithm for bioacoustic signal processing. The proposed method represents an early step toward quantum-enhanced diagnostic systems for resource-constrained healthcare environments.