Improved human identification method based on electrocardiogram using ensemble empirical mode decomposition and Teager Energy Operator

Abstract

The purpose of this research is to develop a biometric system for individual identification with the electrocardiogram (ECG) signal. The ECG signal varies from person to person and it can be used as a new biometric for individual identification. This paper presents a robust preprocessing stage to eliminate the effect from noise and heart rate. A new feature extraction technique known as Ensemble Empirical Mode Decomposition (EEMD) with Teager Energy Operator (TEO) is derived and used to generate novel ECG feature vectors. The dimensionality reduction method Principal Component Analysis (PCA) is used reduce the feature space before classification. Finally, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithm are chosen as the classifiers. The proposed method is validated by experiments on 40 subjects from three public databases; the experiment results show that the subject recognition rate achieves 95.5% and 97.5% with KNN and SVM classifier respectively. For larger changes in heart rate, it also shows strong stability.