Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Abstract

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

“…To solve this problem, a QRS-centered resampling strategy is proposed in this section. Based on the research of [5,20,21], our idea is that any temporal and morphological information of the QRS complex may potentially contribute to ECG identification. Meanwhile, only the amplitude is useful in identifying a subject for the rest part.…”

“…Compared with Method 1, this method further performs alignment of key fiducial points (R). It is firstly proposed by reference [20] and has proved its effectiveness on heart robustness without requiring HR measurement. All the experiments are repeated 10 times.…”

“…To solve this problem, Wonki Lee et al [20] proposed a novel partial-fiducial method, whose idea was that one heartbeat was resampled and mapped into a regular interval by ignoring temporal period information. Taking a pattern matching algorithm based on Euclidean distance as the classifier, they achieved a maximum performance of 98.36% accuracy using finger ECG data.…”

“…However, according to the research of Mikhail Matveev1 et al [21], QRS negative area, total area, slope from R to S peak and sum of the absolute QRS velocities values have a strong correlation between ECG recordings acquired 5 years apart. These features will be distorted during the resampling process proposed by [20], hindering a further increase of identification accuracy. Identification performance can be potentially improved by appropriately reserving the original information of the QRS complex.…”

“…The aim of this study is to propose an HR robust ECG identification method suitable for short-term ECG signals. Our work contains two main parts: (1) On the basis of the research of reference [20], we propose a QRS-centered resampling strategy for heartbeat processing. The method aims to completely preserve the original temporal and morphological information of the QRS complex while solving the problem of T wave shift.…”