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.
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.
Cardiovascular disease has become a major public health issue as the leading cause of death. Timely and accurate detection of heart disease is critical to prevent additional harm to patients. Recent studies show that the implementation of datadriven approaches in medicine, including machine learning (ML) and deep learning (DL), significantly improves the speed and accuracy of heart disease diagnosis. This study proposes a hybrid deep learning model that integrates traditional convolutional neural networks (CNNs) with quantum computing to improve the accuracy of the prediction of cardiovascular diseases. The model uses a parameterized quantum circuit (PQC) to encode patient data, including ECG signals and cardiac images, into quantum states to extract quantum features. Quantum gates modify quantum states to extract complex and high-dimensional features that may be beyond the capabilities of conventional algorithms. A CNN uses convolutional filters to detect localized patterns, including arrhythmias and structural heart abnormalities, to diagnose cardiovascular disease. The CNN then receives the output from the PQC. Integrating quantum feature extraction with conventional deep learning improves prediction accuracy and robustness, facilitating the analysis of high-dimensional and spatially dependent data. The ability of the hybrid model to identify cardiovascular diseases in their early stages may enable faster and more accurate diagnostic results, making it an innovative tool for personalized medicine.
Cardiovascular diseases (CVD) remain a leading cause of morbidity and mortality worldwide, with arrhythmia being one of their common manifestations. Traditional diagnostic methods, such as electrocardiography (ECG), rely heavily on expert interpretation for detecting irregular heart rhythms, which can lead to inaccuracies and delays in diagnosis, particularly in resource-limited settings. In recent years, machine learning (ML) has emerged as a powerful tool for improving the accuracy and efficiency of medical diagnostics. Although deep learning models have demonstrated remarkable success in various domains, their computational complexity poses challenges for real-time applications. This study investigates various quantum-classical hybrid algorithms for improving ECG-based arrhythmia classification. In this study, we employed three hybrid quantum-classical algorithms for arrhythmia detection, estimator-based Quantum Neural Network (QNN), sampler-based QNN, and Quantum Support Vector Classifier (QSVC), to enhance the accuracy of automated arrhythmia classification. The models are evaluated using the MIT-BIH arrhythmia database. The experimental results demonstrated that these three quantum models outperform traditional Recurrent Neural Networks (RNNs) and their variants in several metrics, including accuracy, sensitivity, and specificity. The QSVC (k = 16) model achieved the highest performance, with an accuracy of 97.7%, F1-score of 97.5%, and area under the receiver operating characteristic curve of 99.4%. Additionally, we developed an interactive application using StreamLit for real-time arrhythmia risk prediction and visual feedback. Overall, the proposed quantum ML model demonstrates significant potential for arrhythmia detection with robust and highly accurate performance, providing a solid foundation for future clinical deployment and integration into wearable devices.
Early and accurate identification of heart conditions from electrocardiogram (ECG) signals is particularly important for ongoing patient health monitoring, yet classical deep learning frameworks have been inadequate at achieving high accuracy and low latency under noisy, real-time conditions. This work focuses on developing a quantum-accelerated cardiac ECG (electrocardiogram) analysis. The study introduces a novel hybrid quantum–classical framework capable of simultaneously performing ECG denoising, feature embedding, and arrhythmia classification with reduced latency and improved robustness under noisy real-time conditions. The one introduced in the article is the hybrid quantum-classical architecture, including Quantum Variational ECG Embedding (QVEE) for high-dimensional morphological representation, Quantum Enhanced Denoising Module (QEDM) for noise suppression and signal distortion, and hybrid quantum classification for arrhythmia recognition. Experiments were conducted on the MIT-BIH Arrhythmia Database, and a corpus with noise augmentation showed that the proposed framework was found to be 99.4% correct with an F1 score of 0.97 and reduced the inference latency by 23.7% compared to state-of-the-art CNN-LSTM, Transformer-based models, and showed higher robustness under the condition of low signal-to-noise ratios. The results show that quantum embeddings tend to improve ECG feature separability and that quantum denoising helps preserve clinically relevant waveform structure (i.e., structure detection), particularly for rare arrhythmias. The proposed framework is a promising approach for establishing real-time quantum-assisted monitoring of the human heart, enabling more reliable early diagnosis in wearable and clinical environments.
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.
The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm's speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine.
This paper explores the use of quantum-based machine learning algorithms for predicting heart disease. Traditional methods face challenges in processing complex healthcare data efficiently. To address this, quantum computing techniques, including dimensionality reduction and feature optimization, are applied. Quantum machine learning models, such as Variational Quantum Classifier (VQC), Quantum Support Vector Classification (QSVC) and Quantum Neural Networks (QNN), leverage these enriched datasets. This convergence of quantum computing and healthcare has the potential to transform medical care, cut costs, and save lives, presenting a significant advancement in predictive medicine.