User Identification System Using 2D Resized Spectrogram Features of ECG

Abstract

Studies have been actively conducted on biometrics technology applying electrocardiogram (ECG) signals, which are more robust against forgeries and alterations than fingerprint and face authentication. The ECG lead-I signals measured using ECG acquisition devices consist of 1D data. Therefore, it has limitations with regard to feature extraction and data analysis. This paper proposes a user-recognition system that extracts multi-dimensional features through 2D resizing based on bi-cubic interpolation, which improves the calculation speed and preserves the original data values after converting the measured ECG into a spectrogram. An ECG measuring device was developed, and the ECGs were measured using the developed device. The proposed system consists of an ECG acquisition step, an ECG signal processing step, a segmentation step, a feature extraction step, and a classification step. For ECG signals, the CU-ECG dataset was created by acquiring ECG lead I signal data from 100 subjects in a relaxed state for a period of 160 s. For three sets of shuffle classes that applied the CU-ECG dataset, the average recognition performance was 93% for the existing algorithm and 88.9% for the parameter adjustment method. The average recognition performance of the proposed user recognition system showed a 0.33% improvement compared to the existing algorithm and a 4.43% improvement compared to the parameter adjustment method.