# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
###### Abstract
However, the performance of these above methods depends on the accurate localization of wave boundaries for QT interval estimation or HR measurement. Though the R and T wave detection techniques have started to provide acceptable results in most cases, detecting P, Q, and S is still challenging [19]. Furthermore, HR measurement of ECG signals requires multiple ECG signals and may not be desirable for systems that use short-term ECG signals for identification. Thus non-fiducial or partial-fiducial identification methods should be investigated. To solve this problem, [PERSON] 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 [PERSON] 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. As a result, more potentially helpful information with less HR influence will be provided for the subsequent steps; (2) For feature extraction, Convolution Neural Network (CNN) has proven its effectiveness in medical research such as health informatics and computed tomography image analysis in recent years [22; 23; 24; 25], e.g., [PERSON] et al. [22] conducted a CNN study for automatic arrhythmia detection and recorded accuracy, sensitivity and specificity of 92.50%, 98.09%,and 93.13%, respectively, for two seconds of ECG segments. In our work, Principal Component Analysis Network (PCANet) proposed by [PERSON] et al. [26], a new kind of CNN that employs PCA to learn multistage filter banks, is adopted as the tool to mine more useful components from the processed heartbeats. By using PCA filters as the convolution kernel, this network is sensitive to the local difference among heartbeats from different subjects. At last, a linear Support Vector Machine (linear-SVM) is used to address the identification for faster training and classification. The rest of this paper is organized as follows: Section 2 illustrates the proposed methodology; Database and experimental results are shown in Section 3; the results of our approach are discussed in Section 4; and Section 5 concludes the paper.