Cardiac arrhythmia is a leading cause of sudden cardiac death, often occurring without warning and requiring early, accurate detection for effective intervention. However, their intermittent nature and reliance on expert ECG interpretation make timely diagnosis difficult, especially at scale. This study proposes a hybrid quantum-classical deep learning algorithm for automated arrhythmia detection using ECG images. The algorithm integrates a pre-trained ResNet50 for feature extraction with a 10-qubit variational quantum circuit, enhancing classification through quantum expressiveness. A dataset of 123,998 images, derived from MIT-BIH and PTB ECG recordings, was used to train the algorithm. Each image was resized to 224×224 pixels, and 10-dimensional ResNet50 features were processed by a quantum layer via PennyLane’s lightning.kokkos simulator. The algorithm achieved 99.98% test accuracy. Training was completed in an efficient manner, demonstrating both high performance and time-conscious execution, positioning quantum-enhanced algorithms as promising tools in real-time diagnostic cardiology.
Cardiac arrhythmia is a leading cause of sudden cardiac death, often occurring without warning and requiring early, accurate detection for effective intervention. However, their intermittent nature and reliance on expert ECG interpretation make timely diagnosis difficult, especially at scale. This study proposes a hybrid quantum-classical deep learning algorithm for automated arrhythmia detection using ECG images. The algorithm integrates a pre-trained ResNet50 for feature extraction with a 10-qubit variational quantum circuit, enhancing classification through quantum expressiveness. A dataset of 123,998 images, derived from MIT-BIH and PTB ECG recordings, was used to train the algorithm. Each image was resized to 224×224 pixels, and 10-dimensional ResNet50 features were processed by a quantum layer via PennyLane’s lightning.kokkos simulator. The algorithm achieved 99.98% test accuracy. Training was completed in an efficient manner, demonstrating both high performance and time-conscious execution, positioning quantum-enhanced algorithms as promising tools in real-time diagnostic cardiology.
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.
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.
Hybrid quantum algorithms for ECG image-based arrhythmia detection are increasingly used, but their performance under lower resolutions and varied training initialisations remains unclear. This study evaluates a hybrid quantum algorithm combining a fully fine-tuned ResNet50 backbone with a 10-qubit variational quantum circuit for six-class ECG arrhythmia classification. The algorithm was assessed on 24,799 held-out ECG images at controlled content resolutions of 80, 112, 160, 224 and 320 pixels and compared with a conventional ResNet50 classifier and a closely parameter-matched classical 10-dimensional bottleneck. At 224 and 320 pixels, all three approaches reached ceiling-level classification performance. Differences became more apparent as spatial information was reduced. At 112 pixels, evaluation across three independent training seeds produced a mean accuracy of 0.9690 ± 0.0124 for the hybrid quantum algorithm, compared with 0.9396 ± 0.0044 for ResNet50 and 0.9580 ± 0.0354 for the classical bottleneck. Corresponding macro-F1 scores were 0.8638 ± 0.0417, 0.8235 ± 0.0062 and 0.8783 ± 0.1033, respectively. Although the hybrid algorithm achieved the highest mean accuracy, the relative performance of the hybrid and classical bottleneck architectures varied across training seeds, indicating sensitivity to optimisation randomness. At 160 pixels, the architectures approached ceiling performance, while at 80 pixels the hybrid algorithm and ResNet50 showed comparable classification behaviour. These findings indicate that the quantum pathway does not provide a consistent classification advantage across resolutions or random initialisations, but remains competitive under moderate spatial-information reduction. Without patient identifiers or external validation, results reflect image-level benchmark performance. Broader generalisability requires patient-independent and external validation.
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.
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.
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.