Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit
Sakr, A., Ahmed, H., Rezk, A. (2024). Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit. EKB Journal Management System, 11(1), 62-76. doi: 10.21608/ijci.2023.227360.1118
Ahmed Sakr; Hatem Ahmed; Abdallah Mohamed Rezk. "Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit". EKB Journal Management System, 11, 1, 2024, 62-76. doi: 10.21608/ijci.2023.227360.1118
Sakr, A., Ahmed, H., Rezk, A. (2024). 'Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit', EKB Journal Management System, 11(1), pp. 62-76. doi: 10.21608/ijci.2023.227360.1118
Sakr, A., Ahmed, H., Rezk, A. Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit. EKB Journal Management System, 2024; 11(1): 62-76. doi: 10.21608/ijci.2023.227360.1118

Robust and Efficient ECG Classification with Privacy Preservation using LSTM+CNN and Adaptive Least Signal Bit

IJCI. International Journal of Computers and Information
Article 6, Volume 11, Issue 1, January 2024, Page 62-76  XML PDF (668.92 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.227360.1118
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Authors
Ahmed Sakr1; Hatem Ahmed2; Abdallah Mohamed Rezk email 3
11 Cybersecurity Department, Engineering and Information Technology College, Buraydah Private Colleges, Buraydah 51418, SaudiArabia
2Faculty of Computer and Information Menoufia University
3Department of Information Technology, Faculty of Industry and Energy Technology, Delta university, Egypt
Abstract
Maintaining patient data privacy is a critical concern in the medical domain. However, safeguarding crucial information during data transmission presents significant challenges for healthcare professionals and organizations. Additionally, the accurate classification of electrocardiogram (ECG) signals is hindered by the need for supplementary information to ensure precise examinations and accurate diagnoses. This paper introduces a novel hybrid framework that addresses both ECG classification challenges and privacy preservation. The proposed framework consists of two main phases. In the first phase, a combination of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with an attention gate is employed for ECG classification. This phase enhances the accuracy of ECG signal classification by incorporating deep learning techniques. The second phase of the framework utilizes adaptive least signal bit with neutrosophic logic to conceal critical medical data during transmission. Neutrosophic sets, which represent data as degrees of truth, falsehood, and indeterminacy, are leveraged and passed through an embedding layer. In the sender part, important data is converted into three degrees and embedded within the ECG signal using true and false degrees. An intermediate set acts as a shared dynamic key between the sender and receiver. The receiver can reconstruct the important data using either the shared dynamic key or the intermediate set. The experiments done prove the efficiency of different parts of the framework; the augmentation model archives 14.67 and 37.89 for the inception score and freshet inception distance, respectively. The hybrid approach also achieves 99.01 and 99.12 for dice score and accuracy, respectively.
Keywords
Adaptive LSB; Neutrosophic; Steganography; Attention Gate; Latent Diffusion Model
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