Multi-Layer Co-Occurrence Matrices for Person Identification from ECG Signals

Recently, numerous researches have been executed to create reliable systems to recognize persons based on their biometric information. As a result, person identification (PI) systems have become popular among researchers using different methods. In recent years, it is seen that Electrocardiogram (ECG) signals have started to be used for biometric systems as well as health-related studies. Because ECG data is unique for each person cannot be imitated or copied for biometric studies, it is advantageous for PI problems compared to other biometric data. In this study, we have conducted a method that uses One Dimensional Multi-Layer Co Occurrence Matrices (1D-MLGLCM) to recognize individuals based on their ECG signals. The dataset used in the experiments contains ECG data of 90 subjects whose ages ranged from 13 to 75 years. First of all, ECG signals are normalized at 32 different intervals for the PI system. Then, Dimensional Co-Occurrence Matrices (1D-GLCM) are applied to each signal to construct co-occurrence matrices. These matrices are used to extract Heralick features to feed classification algorithms such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Bayes Net (BN), and K-Nearest Neighborhood (KNN). Our proposed method achieved a 93.414% success rate by using SVM. As a result, the study proves that the suggested method has achieved very effective outcomes by using ECG signals for person identification problems.