Secure and Energy-Efficient ECG Signal Monitoring in the IoT Healthcare using Compressive Sensing

Compressive Sensing (CS) has emerged as a popular signal processing technique, finding diverse applications in biomedical signal processing such as electrocardiogram (ECG or EKG), electroencephalogram (EEG), and electromyogram (EMG). CS enables direct analog-to-information conversion, facilitating simultaneous sampling, compression, and encryption. In the context of Internet of Things (IoT), where security and energy efficiency are critical, CS can be seen as a cryptosystem to achieve these objectives while ensuring the secrecy of the sampling/measurement matrix. In the realm of IoT, it is imperative to employ advanced data acquisition techniques that optimize power consumption in wireless sensor nodes while simultaneously ensuring the integrity and security of data transmission. Addressing the energy constraints of sensing devices and the need for secure wireless communication, we propose a CS-based energy-efficient and secure ECG monitoring and communication system based on CS. This paper focuses on utilizing two approaches to reduce the sampling rate along with encrypting the signal without incurring in additional computational costs. The proposed method is evaluated through experimental results, showcasing its ability to provide both optimal security and compression simultaneously, thereby conserving power in the sensing node. Our work aims to contribute to the advancement of secure and energy-efficient healthcare monitoring in the context of IoT.