Privacy Assured Recovery of Compressively Sensed ECG Signals

Hadi Zanddizari,Sreeraman Rajan,Hassan Rabah,Houman Zarrabi

doi:10.1109/access.2022.3149890

Abstract

In the areas of communications engineering and biomedical engineering, cloud computing for storing data and running complex algorithms have been steadily increasing due to the increase in internet of things and connected health. As connected IoT devices such as wearable ECG recorders generally have less storage and computational capacity, acquired signals get sent to a remote center for storage and possible analysis on demand. Recently, compressive sensing has been used as a secure, energy-efficient and fast method of signal sampling in such recorders. In this paper, we propose a secure procedure to shift away the total recovery of compressively sensed measurement to cloud and introduce a privacy-assured signal recovery technique in the cloud. We present a fast, and lightweight encryption for secure CS recovery outsourcing that can be used in wearable devices, such as ECG Holter monitors. In the proposed technique, instead of full recovery of CS-compressed ECG signal in the cloud, to preserve privacy, an encrypted version of ECG signal is recovered by using a randomly bipolar permuted measurement matrix. The user with a key, decrypts the encrypted ECG from the cloud to obtain the original ECG signal at their end. We demonstrate our proposed method using the ECG signals available in the MIT-BIH Arrhythmia Database. We also demonstrate the strength of the proposed method against partial exposure of the key. Experimental results on client and cloud sides show our proposed method has lower complexity and consuming time compared to the recent related works, while maintaining the quality of outsourcing task in cloud.

Highlights

I N biomedical area, there are equipment and devices that produce huge amount of data
Three such metrics that are commonly used for assessing the quality of recovered ECG signals are percentage root-mean-square difference (PRD), the normalized version of PRD namely PRDN, and signal to noise ratio (SNR), P RD[%] = 100
When ECG measurements are transmitted to the cloud, the cloud with its strong resources can do the compressive sensing (CS) recovery for the client

Summary

Introduction

I N biomedical area, there are equipment and devices that produce huge amount of data. The ECG data produced by devices such as a Holter monitor is large and need to be stored for analysis and tracking improvements in the physiology for medical interventions to be undertaken. Under such circumstances, compression can be used for efficient use of communication channel bandwidth and storage in such devices. Compressive sensing (CS) is a sampling technique for efficiently sampling a signal by solving under-determined linear systems [1], [2] It takes advantage of the signal’s sparsity, and the signal can be effectively represented by fewer number of measurements than the Nyquist rate. Compression phase in CS provides the measurement vector through a linear operation as given below:

Methods

Results

Conclusion