MedQNet: a quantum-integrated blockchain framework for secure and intelligent EEG-based seizure detection in IoMT healthcare

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.