Express method of biometric person authentication based on one cycle of the ECG signal

Abstract

The article is devoted to an express method of biometric authentication of a person based on an electrocardiogram (ECG). The method is characterized by high accuracy (efficiency) of authentication of a person based on only one cycle of its ECG. Such characteristics as Accuracy, Balanced Accuracy and F1-score on average are not lower than 96.1% for such binary classifiers as k-Nearest Neighbors, Linear SVM, Decision Tree, Random Forest, Multilayer Perceptron, Adaptive Boosting, Naive Bayes and Statistical Interval Classifier. The research utilized the Combined Measurement of ECG, Breathing, and Seismocardiograms database, which features data from 20 healthy people. A method of constructing confidence intervals for ECG cycles has been developed, which is based on the rhythm-adaptive statistical estimation of the mathematical expectation and the standard deviation of the ECG signal. The method of constructing confidence intervals is based on the functioning of the Statistical Interval Classifier in the system of biometric authentication of a person. The Statistical Interval Classifier has the lowest time computational complexity among the 8 studied classifiers, which justifies its use in portable biometric authentication systems that have negligible computing resources.