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.
Highlights
Electrocardiogram (ECG) has become a popular tool in analyzing heart disease with the use of telemedicine and home care techniques [1, 2]
For a sparse matrix M which came from one unidentified individual, we compute the correlation coefficient of this sparse matrix M and each individual to get corresponding Ri
A new ECG identification method is proposed with two-dimensional sparse matrix algorithm, in which twolead ECG signals are fused using a sparse matrix approach
Summary
Electrocardiogram (ECG) has become a popular tool in analyzing heart disease with the use of telemedicine and home care techniques [1, 2]. ECG is useful as a diagnostic tool and has been applied on information watermarking [3,4,5], data compression [3, 6], and human identification [7,8,9,10,11,12,13,14,15]. 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.
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