This paper introduces a novel Compressive Sensing (CS)-based cryptosystem tailored for the secure and efficient transmission of Electrocardiogram (ECG) signals in Internet of Medical Things (IoMT) environments. It leverages the inherent sparsity of ECG signals in the wavelet domain and ensures both data privacy and integrity during transmission through a simple but effective additional encryption stage. We have evaluated the performance of four distinct sensing matrices and three reconstruction algorithms across multiple wavelet families. Through extensive simulations using the MIT-BIH Arrhythmia Database we demonstrate that the Low-Density Parity-Check matrix combined with the L1 optimization algorithm achieves the highest Quality Score, with Compression Ratio up 50%. The proposed approach also shows an excellent ability in preserving important pathological features in presence of abnormal beats. The proposed CS encoder has been hardware implemented on low-resource microcontroller and FPGA devices. When realized on a Xilinx Artix 7 XC7A12 T FPGA, such a prototype allows real-time operations to be sustained running at 1 MHz clock frequency and dissipating only 0.8nJ per sample.
A Novel Compressive Sensing Method for Secure and Energy Efficient ECG Signal Transmission Applications
Spagnolo F.;Lal B.;Corsonello P.;Gravina R.
2025-01-01
Abstract
This paper introduces a novel Compressive Sensing (CS)-based cryptosystem tailored for the secure and efficient transmission of Electrocardiogram (ECG) signals in Internet of Medical Things (IoMT) environments. It leverages the inherent sparsity of ECG signals in the wavelet domain and ensures both data privacy and integrity during transmission through a simple but effective additional encryption stage. We have evaluated the performance of four distinct sensing matrices and three reconstruction algorithms across multiple wavelet families. Through extensive simulations using the MIT-BIH Arrhythmia Database we demonstrate that the Low-Density Parity-Check matrix combined with the L1 optimization algorithm achieves the highest Quality Score, with Compression Ratio up 50%. The proposed approach also shows an excellent ability in preserving important pathological features in presence of abnormal beats. The proposed CS encoder has been hardware implemented on low-resource microcontroller and FPGA devices. When realized on a Xilinx Artix 7 XC7A12 T FPGA, such a prototype allows real-time operations to be sustained running at 1 MHz clock frequency and dissipating only 0.8nJ per sample.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
