A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition.
作者： Xiao , Hu , Shao , Li
DOI: 10.3390/S19235330
关键词: Computer vision 、 Biometrics 、 Signal compression 、 Signal 、 Sampling (signal processing) 、 Artificial intelligence 、 Signal reconstruction 、 Data compression 、 Matching pursuit 、 Compressed sensing 、 Computer science
摘要: Biometric systems allow recognition and verification of an individual through his or her physiological behavioral characteristics. It is a growing field research due to the increasing demand for secure trustworthy authentication systems. Compressed sensing data compression acquisition method that has been proposed in recent years. The sampling completed synchronously, avoiding waste resources meeting requirements small size limited power consumption wearable portable devices. In this work, reconstruction based on was studied using bioelectric signals, which aimed increase remote signal equipment. Using electrocardiograms (ECGs) photoplethysmograms (PPGs) heart signals as data, improved segmented weak orthogonal matching pursuit (OMP) algorithm developed compress reconstruct signals. Finally, feature values were extracted from reconstructed identification analysis. accuracy practicability cardiac verified. Experiments showed ECG PPG rates 95.65% 91.31%, respectively, residual value less than 0.05 mV, indicates can be effectively used two reconstructions.
mdpi.com
sci-hub.se 参考文章(34)
Gábor Andrássy, [The effect of various stressors on the QT-interval and the T-wave]. Orvosi Hetilap. ,vol. 150, pp. 447- 457 ,(2009) , 10.1556/OH.2009.28537
Francesco Gargiulo, Antonio Fratini, Mario Sansone, Carlo Sansone, Subject identification via ECG fiducial-based systems: influence of the type of QT interval correction. Computer Methods and Programs in Biomedicine. ,vol. 121, pp. 127- 136 ,(2015) , 10.1016/J.CMPB.2015.05.012
Li Zhang, Katherine W. Timothy, G. Michael Vincent, Michael H. Lehmann, Jolene Fox, Lisa C. Giuli, Jiaxiang Shen, Igor Splawski, Silvia G. Priori, Steven J. Compton, Frank Yanowitz, Jesaia Benhorin, Arthur J. Moss, Peter J. Schwartz, Jennifer L. Robinson, Qing Wang, Wojciech Zareba, Mark T. Keating, Jeffrey A. Towbin, Carlo Napolitano, Aharon Medina, Spectrum of ST-T–Wave Patterns and Repolarization Parameters in Congenital Long-QT Syndrome ECG Findings Identify Genotypes Circulation. ,vol. 102, pp. 2849- 2855 ,(2000) , 10.1161/01.CIR.102.23.2849
Guohua Lu, John-Stuart Brittain, Peter Holland, John Yianni, Alexander L. Green, John F. Stein, Tipu Z. Aziz, Shouyan Wang, Removing ECG noise from surface EMG signals using adaptive filtering Neuroscience Letters. ,vol. 462, pp. 14- 19 ,(2009) , 10.1016/J.NEULET.2009.06.063
Steven A. Israel, John M. Irvine, Andrew Cheng, Mark D. Wiederhold, Brenda K. Wiederhold, ECG to identify individuals Pattern Recognition. ,vol. 38, pp. 133- 142 ,(2005) , 10.1016/J.PATCOG.2004.05.014
Siguang Chen, Meng Wu, Kun Wang, Zhixin Sun, Compressive network coding for error control in wireless sensor networks Wireless Networks. ,vol. 20, pp. 2605- 2615 ,(2014) , 10.1007/S11276-014-0764-4
SEUNGJAE LEE, Jun Luan, Pai Chou, A new approach to compressing ECG signals with trained overcomplete dictionary international conference on wireless mobile communication and healthcare. pp. 83- 86 ,(2014) , 10.4108/ICST.MOBIHEALTH.2014.257383
Gesen Zhang, Shuhong Jiao, Xiaoli Xu, Lan Wang, None, Compressed sensing and reconstruction with bernoulli matrices international conference on information and automation. pp. 455- 460 ,(2010) , 10.1109/ICINFA.2010.5512379
André Lourenço, Hugo Silva, Ana Fred, Unveiling the biometric potential of finger-based ECG signals Computational Intelligence and Neuroscience. ,vol. 2011, pp. 1- 8 ,(2011) , 10.1155/2011/720971
A. Khazaee, A. Ebrahimzadeh, Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features Biomedical Signal Processing and Control. ,vol. 5, pp. 252- 263 ,(2010) , 10.1016/J.BSPC.2010.07.006