IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

Area of Science:

• Quantum Computing
• Medical Informatics
• Cardiovascular Health

Background:

• Electrocardiogram (ECG) leakage is a growing concern with the Internet of Medical Things.
• Classical blockchain security is insufficient for ECG data in the quantum era.
• Secure and efficient arrhythmia detection is crucial for cardiovascular disease diagnosis.

Purpose of the Study:

• To propose a Quantum Arrhythmia Detection System (QADS) for secure ECG data storage and sharing.
• To develop a quantum-based approach for recognizing abnormal ECG data and diagnosing cardiovascular diseases.
• To enhance the security and practicality of ECG data management using quantum blockchain technology.

Main Methods:

• Implementation of a quantum blockchain algorithm with a controlled quantum walk hash function and quantum authentication protocol.
• Construction of a hybrid quantum convolutional neural network (HQCNN) for extracting temporal ECG features.
• Utilizing quantum neural networks for abnormal ECG data recognition within the QADS framework.

Main Results:

• The HQCNN achieved an average training accuracy of 94.7% and testing accuracy of 93.6%.
• HQCNN demonstrated superior detection stability and robustness against quantum noise compared to classical CNNs.
• Mathematical analysis confirmed the quantum blockchain algorithm's strong security against quantum attacks.

Conclusions:

• QADS offers a secure and practical solution for ECG data storage and sharing using quantum blockchain.
• The HQCNN effectively detects abnormal heartbeats, improving cardiovascular disease diagnosis.
• The proposed system provides robust security against sophisticated quantum attacks, ensuring data integrity.