Key result
Privacy-preserving 1D CNN framework achieves ~98% arrhythmia classification accuracy while securing EHRs via blockchain.
Why the study?
Blockchain technology creates a secure, decentralized, and reliable environment for collecting and processing healthcare data, making it an appropriate method for protecting sensitive healthcare information.
A novel framework combining blockchain, decentralized storage, and a 1D CNN achieved 98.16% accuracy for arrhythmia classification while ensuring data privacy and immutability.
Answers are grounded in this paper and cite their sources.
Proposed blockchain-DL EHR framework requires prospective validation; leaves open real-world scalability and clinical adoption.
The use of Blockchain technology allows for the creation of a secure, decentralized, and reliable environment for the collection and processing of health care data; therefore, it is an appropriate method for the protection of sensitive health care information. This paper proposes a privacy-preserving framework for Electronic Health Record (EHR) management, integrating permissioned Blockchain technology, decentralized IPFS storage, and Deep Learning (DL). The architecture provides strong data integrity and traceability through blockchain-based audit logging, dynamic AES-256-CBC encryption, and application-layer access controls that eliminate single points of failure. Utilizing the MIT-BIH Arrhythmia dataset, we evaluate multiple DL architectures—including recurrent networks, Transformers, and 2D transfer learning models—against a highly parameter-efficient 1D Convolutional Neural Network (CNN). By utilizing Discrete Wavelet Transform (DWT) denoising and concurrent IPFS thread-pool batching, the framework achieves latency-resistant, edge-deployable diagnostic capability without exposing raw physiological waveforms to the global ledger. Among all the models we evaluated, the CNN model provided the highest level of accuracy at 98.16% followed by the CNN-LSTM model and the Transformer Model which were also highly accurate as well as being very effective across different DL architectures. Our most significant contribution was the combination of blockchain secured, decentralized storage with the use of advanced DL based arrhythmia classification providing both high levels of diagnostic accuracy and immutability and privacy preserving EHR Management. Therefore, this framework demonstrates the potential to enhance secure, transparent, and regulation-compliant patient data management, building trust between patients and AI-driven diagnostics. However, further validation on real-world clinical datasets is required before practical adoption.
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Manwal et al. (2026) studied Arrhythmia. Privacy-preserving framework integrating permissioned Blockchain, IPFS, and 1D CNN vs. Recurrent networks, Transformers, and 2D transfer learning models was evaluated on Diagnostic accuracy. A privacy-preserving framework utilizing a 1D Convolutional Neural Network achieved 98.16% diagnostic accuracy for arrhythmia classification while securing electronic health records via blockchain.
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