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IoMT-based smart he...
IoMT-based smart healthcare detection system driven by quantum blockchain and quantum neural network
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- Qu, Zhiguo (author)
- Nanjing University of Information Science and Technology, Nanjing, China
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- Shi, Wenke (author)
- Nanjing University of Information Science and Technology, Nanjing, China
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- Liu, Bo (author)
- Hubei University of Science and Technology, Xianning, China
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- Gupta, Deepak (author)
- Maharaja Agrasen Institute of Technology, New Delhi, India; Chandigarh University, Mohali, India
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- Tiwari, Prayag, 1991- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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(creator_code:org_t)
- Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2024
- 2024
- English.
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In: IEEE journal of biomedical and health informatics. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 28:6, s. 3317-3328
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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 (IoMT). 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 named 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 nameded 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. © IEEE
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
Keyword
- Arrhythmia
- Arrhythmia detection
- Blockchain
- Blockchains
- Electrocardiography
- Heart beat
- Internet of Medical Things
- Medical services
- Neural networks
- Quantum neural network
- Security
- Smart healthcare
Publication and Content Type
- ref (subject category)
- art (subject category)
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