Individual Identification Using Linear Projection of Heartbeat Features

Abstract EN

This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification.

The ECG characterization is performed using an automated approach consisting of analytical and appearance methods.

The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace.

We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects.

Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts.

The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database.

These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.