No abstract

No abstract

Evolving digital technologies in remote health monitoring require an energy-efficient method for secure and reliable transmission of patient's/user's confidential information from the sensor nodes to the cloud/medical server. Thus, a united scheme of the physiological signal steganography and its communication by benefitting from the unequal significance between different parts of the physiological data are emphasized. We believe higher steganography coding strength and more robust source-channel coding would protect extremely vital parts of the physiological data. Therefore, data integrity and transmission efficiency of packet information achieved in a resilient way. We formulate our idea of joint steganography-source-channel coding (JS 2 C 2) as an optimization problem to simultaneously securing and minimizing the transmission energy consumption. A low-complexity deep learning-based ECG classification algorithm along with its secure and energy-efficient neural JS 2 C 2 transmission for real-time monitoring has been realized. The optimal parameters for our united framework have been calculated by JS 2 C 2 optimization method. Our steganography algorithm unequal steganography embedding (USE) achieves very low wavelet-based weighted percent root-mean-squared difference lower than 0.5%. Furthermore, the high correlation between cover and stego and low end-to-end mean-square error (MSE) indicates resilient imperceptibility and maintains the diagnosability of the physiological signal. Moreover, low MSE between embedded and extracted data validates that embedded confidential data has been extracted with negligible distortion. In addition, for the given distortion, the USE-based framework's energy consumption is much smaller (by 55% in typical application scenario) as compared with the equal steganography embedding-based approach's energy consumption. Keywords Electrocardiography (ECG) • Unequal error protection (UEP) • Unequal steganography embedding (USE) • Joint steganography source coding (JS 2) • Joint source-channel coding (JSC 2) • Joint steganography-source-channel coding (JS 2 C 2) • Deep learning (DL) • Security • Correlation • End-to-end distortion • Energy efficiency

The developing prominence of edge computing and Internet of Things-based wearables coupled with body sensors offer us a unique idea of integrating the Diagnosis, Steganography, and Transmission tasks in the healthcare domain. In this paper, we present an innovative Diagnosis-Steganography-Transmission architecture for health monitoring and real-time diagnosis especially designed for Coronary Artery Disease diagnostic purposes. The architecture works by extracting the patient's health state by a real-time execution of a preliminary diagnostic algorithm in the local embedded computing platform before the ECG data are transmitted over wireless networks for further analysis. Local pre-diagnosis assists the operation of the communication module in deciding when and how much data should be transmitted and the given quality. The novelty of the proposed work is that the integration of diagnosis, steganography, and communication tasks in a unified platform, because unequal importance of physiological signals (e.g., ECG) feature offers computing distribution of diagnosis, UEP-based steganography, and UEP-based transmission are inherently connected with each other. Using the proposed framework, the steganography embedder, source encoder, and channel encoder in the communication module effectively reconfigure the intricacy of the control factors to match the energy constraints while maintaining the reconstruction quality of the medical signal. Moreover, the diagnosis module also reconfigures the complexity of the process of diagnosis to match the communication bandwidth constraints. By deep CNN-based ECG classification for local pre-diagnosis, an immense heap of energy-saving is created by a huge diminishing in the transmission overhead (up to 99.3% in typical application set-up) as compared to always-on communication.

No abstract