Human identification using a new matching Pursuit-based feature set of ECG. (April 2019)

• Record Type: Journal Article
• Authors: Goshvarpour, Ateke; Goshvarpour, Atefeh
• Is Part Of: Computer methods and programs in biomedicine. Volume 172(2019)
• Page Start: 87
• Page End: 94
• Publication Date: 2019-04
• Subjects: Human identification -- Electrocardiogram -- Matching pursuit -- Classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.02.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An accurate identification system using a novel one-lead ECG feature set is proposed. The proficiency of linear and nonlinear indices extracted from matching pursuit coefficients is investigated. The highest recognition rate of 99.68% is achieved for human identification. Abstract: Background and Objective: In recent years, many attempts have been made to design reliable systems for identifying individuals using biometrics. Electrocardiogram (ECG) biometric is one of the newest methods that not only offers unique characteristics of individuals for human identification, but also the possibility of counterfeiting it is negligible. In this paper, our objective was to develop an identification system using a non-fiducial one-lead ECG feature set based on a sparse algorithm. Methods: The ECG signals of 90 participants were decomposed using a matching pursuit (MP) and several statistical and nonlinear measures were extracted from the MP coefficients. Then, the performance of ECG characteristics delivered by MP analysis in human identification was evaluated by the probabilistic neural network (PNN) and k-nearest neighbor (kNN) with one vs. all strategy. The role of the feature set in classification rates was also tested in different modes, including linear attributes, nonlinear indices, all features, features selected by principal component analysis (PCA), and features selected by linear discriminant analysis (LDA). Results: Experimental results showed that (1) the highest recognition rate was 99.68%; (2) the performance of the PNN was superior to the kNN; and (3) selecting features with LDA resulted in higher identification rates. Conclusions: The results are prominent from the performance perspective because it gives higher recognition rates over the group of 90 participants. The great performance of the proposed identification system advocates that it can be employed confidently in different smart systems.