The Scientific World Journal

Volume 2015, Article ID 656807, 9 pages

http://dx.doi.org/10.1155/2015/656807

## Sparse Matrix for ECG Identification with Two-Lead Features

^{1}Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong 518052, China^{2}School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK^{3}School of Electronic and Communication, Shenzhen Graduate School, Peking University, Shenzhen, Guangdong 518052, China

Received 26 March 2014; Revised 6 October 2014; Accepted 27 October 2014

Academic Editor: Zhaojie Ju

Copyright © 2015 Kuo-Kun Tseng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

#### 1. Introduction

Electrocardiogram (ECG) has become a popular tool in analyzing heart disease with the use of telemedicine and home care techniques [1, 2]. However, ECG not only is useful as a diagnostic tool but also has been applied on information watermarking [3–5], data compression [3, 6], and human identification [7–15]. ECG techniques have the potential to play a role in biometric identification.

Existing biometric identification techniques have focused on the use of fingerprints, facial geometry, and voice analysis. We may be able to apply ECG techniques to protect health care systems from data leakage.

In this work, we propose a new algorithm using two leads of ECG signals for human identification. This algorithm uses the sparse matrix for dimensionality reduction that mapped two-lead data into one coordinate. We take advantage of the sparse matrix for identification. Our algorithm is sparse matrix correlation coefficient (SMCC).

Through the experiment, we demonstrate that our approach is more accurate for human identification and verification than existing techniques. In a summary, compared to the previous ECG identification, our approach has the following advantages.

Using sparse matrix to store data that contains a large number of zero-valued elements can both save a significant amount of memory and speed up the processing of that data.

The algorithm performs rapidly with lower computational complexity than the PCA method to process two-lead signals.

The remainder of this paper is organized as follows: Section 2 contains an overview of related work in ECG identification; Section 3 introduces the proposed ECG identification algorithm; the experimental results are presented in Section 4; finally, some concluding remarks are stated in Section 5.

#### 2. Related Work

For ECG identification, research has focused on areas such as signal preprocessing, feature extraction, data classification, data reduction, and intelligence optimization.

Based on our survey, the ECG feature extraction algorithms can be classified into two categories: transform-based [9, 13, 15] and waveform-based [7, 8, 11, 12]. The transform-based algorithms consist of transforms in wavelet [15] and frequency domain. Since the wavelet transform contains information in both the time and frequency domains, it is more popular than the frequency based techniques which include Fourier transform [13] and discrete cosine transform (DCT). Waveform-based method measures the distance and amplitude difference between wave peaks and valleys. These attributes represent certain characteristics of the signal, such as in [8]; morphological characteristics are first extracted through the wavelet transform.

But some approaches are hybrid, for example [8], using morphological characteristics which are extracted through the wavelet transform. The feature extraction of our approach could be a hybrid approach as well; it transfers two waveform signals into a two-dimensional space then measures their similarity in the two-dimensional space.

Most approaches to ECG identification only use one-lead signal [7, 10, 14]. Lead systems allow you to look at the heart from different angles. Each different angle is called a lead. The different leads can be compared to radiographs taken from different angles. So we can use more than one feature to classify discrimination. In many characteristics classification, there are many kinds of features.

Two-dimensional processing of ECG data has been applied in compression and diagnostic areas. The authors of [16] proposed diagnosis of acute myocardial infarction using two-dimensional echocardiography. The authors of [17, 18] implemented ECG data compression on two-dimensional data. These approaches have however to our knowledge not been applied to ECG identification.

In a summary, most of the work on ECG biometrics made use of only one lead and ignored the other leads that may contain additional information. The ECG signals from the two leads are essentially two observations of the same physiological activity from two different perspectives. Thus, we proposed a new two-lead algorithm for ECG identification.

Data computation is another area that we reviewed when considering existing approaches to ECG identification. A common approach is correlation of coefficients for measurements of feature distance, such as the wavelet distances that have been used in matching acquired ECG signals for identification [9, 15]. The work [19] applied the feature set evaluation (FSE) with* k*-nearest neighbor (*k*-NN) algorithm to improve low recognition rates and used the eigen-space method to reduce data dimensions; however, this approach is both complicated and time consuming. By using typical neural classifier, the research [11, 20] is applying the neural network in ECG identification.

Further, one popular approach is PCA [21] which is an analogue of the principal axes theorem in mechanics, and it was later independently developed by [22]. A recent application of PCA in ECG signal processing is useful feature reduction of various ECG properties [1, 2, 8, 23].

In this work, we adapt spare matrix [24] for ECG identification, and it was invented as early as a century ago; CF Gauss, CGJ Jacobi, and others have studied the use of matrix sparse in some ways. Linear programming and numerical solution of boundary value problems had been apply for sparse problems in 1950s. DM Young and RS Varga on iterative research process can also be seen as the result of high-level sparse problem. But modern sparse matrix technology is mainly developed since the 1960s, and in the early and mid-60’s some researchers studied the direct method as a starting point. Sparse matrix has penetrated into many areas of research. For example, in structural analysis, network theory, power distribution systems, chemical engineering, photography, surveying and mapping, and other aspects of management science studies have appeared until hundreds of thousands of rank-order sparse matrix.

But according to our survey, we did not find any one to transform ECG signal into two-dimensional space and fuse with sparse matrix. In this research, we also found that they work well for the similarity measurement in ECG identification.

#### 3. Algorithm

In this work, we target the two-lead ECG signal to be transformed into two-dimensional coordinates and perform the identification using sparse matrix. Figure 1 shows the flow of utilizing sparse matrix in ECG human identification system, which consists of three steps. First, we map the ECG two-lead signals into two-dimensional coordination that forms a matrix. Then, we reduce dimensions of the matrix using a special mask, the size of which depends on how many dimensions we want to reduce. We then transfer the matrix into a sparse matrix so that it can be stored and addressed easily. The sparse matrix is regarded as the fusion features of ECG two-lead signals. Finally, the feature data for various individuals are used to train the sparse matrix classifier. Figure 1 is the detailed formula for the procedure.