Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis

Abstract

Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

“…Anyway, a systematic approach of compressed learning of ECG signal had not been done, but few have implemented compressed learning on ECG. Ching-Yao Chou et al [230] proposed a biometric user identification using ECG known as Compressed alignmentaided compressive analysis (CA-CA) algorithm, this CA-CA algorithm uses PCA based dictionary in compressed domain, where the reconstruction of ECG signal is avoided and the information is directly recovered from compressed domain resulted in a reduction of computation time by 81.08% and classified the compressed ECG signal with high accuracy. Compared to compressed learning (CL), the accuracy of the proposed algorithm was improved by 7.11%, the accuracy dropped with respect to Reconstruction learning with alignment (RL-A) and this algorithm was tested only on a small ECG database.…”

A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification

“…CS combined with machine learning on ECG signal classification was simulated in the study. [ 14 15 ] The results of their study showed high accuracy in the compressed signal. The use of CS in other medical signal classifications such as EEG epileptic detection has been reported in studies.…”