Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.
Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.
The emergence of Internet of Medical Things (IoMT) for medical data collection and pre-processing has raised concerns regarding the privacy and security of the data. For many years, the decentralized technology of blockchain has ensured that data is secure and protected from intruders. However, with the emergence of quantum computing, blockchain is vulnerable because it relies on traditional cryptography. Therefore quantum-based blockchain emerged as a solution, it provides more security and ensures data integrity. In this study, MedQNet, a quantum-based platform integrating Quantum-based blockchain to store medical records, and additionally a Quantum-based cloud processing is proposed for secure medical data processing while preserving user privacy. A quantum-aided convolutional neural network (QACNN) is employed to analyze and extract patterns from EEG signals for normal and seizure brain activity detection, demonstrating superior stability and accuracy under quantum noise. Furthermore, a hierarchical quantum mechanics-based framework processes EEG signals for feature extraction, followed by an improved hybrid QACNN with arbitrary nonlinear kernels for classification, achieving exponential speedup over classical methods. The analytical assessment confirms that the quantum ledger framework effectively resists various quantum-based risks, including external intrusions, entanglement-based assaults, and interception-measure-replay attacks. By combining the security strengths of quantum blockchain with the computational power of quantum machine learning, this integrated approach ensures reliable, high-performance processing of EEG signals in IoMT, advancing privacy-preserving performance that reaches up to 96.78% accuracy for healthcare solutions.
A substantial spike in the need for quality healthcare has resulted from people being more cognizant of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR)system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. Automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) with controlled activities. One qubit is used to store the hash value of each block. A lot of information regarding quantum internet is included in the protocol for the entangled quantum medical record (EQMR). EQMR can be accessed in Medical Internet of Things (M-IoT) systems may be kept private and secure, and their where-abouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as the External attack, Intercept-Measure-Repeat attack, and Entanglement-Measure attack. The reliability and auditability evaluations of the entangled BloQ, as well as the quantum circuit design for computing the hash value, are presented. There's also a comparison between the suggested approach and several other quantum blockchain designs.
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.
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.
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 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.