Lightweight Lossy Compression of Biometric Patterns via Denoising Autoencoders

IEEE Signal Processing Letters

Volumn 22, Issue 12, 2015, Pages 2304-2308

  Del Testa, Davide ^a   Rossi, Michele ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Autoencoders; biometric patterns; lossy compression; wearable devices

Indexed keywords

ALGORITHMS; BIOMETRICS; BLOOD PRESSURE; CONSTRAINED OPTIMIZATION; LEARNING SYSTEMS; WEARABLE TECHNOLOGY;

AUTOENCODERS; BIOMETRIC PATTERNS; INTERNET OF THINGS (IOT); LOSSY COMPRESSIONS; PERSONALIZED HEALTHCARE; RECONSTRUCTION ERROR; TRANSMISSION OF DATA; WEARABLE DEVICES;

INTERNET OF THINGS;

EID: 84960156366     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2015.2476667     Document Type: Article

References (22)

1. D. Zordan, B. Martinez, I. Villajosana, M. Rossi, "On the performance of lossy compression schemes for energy constrained sensor networking," ACM Trans. Sensor Netw., vol. 11, no. 1, pp. 15:1-15:34, 2014.
2. B. A. Rajoub, "An efficient coding algorithm for the compression of ECG signals using the wavelet transform," IEEE Trans. Biomed. Eng., vol. 40, no. 4, pp. 355-362, 2002.
3. F. Castells, P. Laguna, L. Sörnmo, A. Bollmann, J. M. Roig, "Principal component analysis in ECG signal processing," EURASIP J. Adv. Signal Process., vol. 2007, no. 1, pp. 1-21, 2007.
4. D. A. DiPersio and E. C. Barr, "Evaluation of the fan method of adaptive sampling on human electrocardiograms," Med. Biol. Eng. Comput., vol. 23, no. 5, pp. 401-410, 1985.
5. Y. Bengio, "Learning deep architectures for AI," Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1-127, 2009.
6. K. Hornik, M. Stinchcombe, H. White, "Multilayer feedforward networks are universal approximators," Neural Netw., vol. 2, no. 5, pp. 359-366, 1989.
7. G. Cybenko, "Approximation by superpositions of a sigmoidal function," Math. Contr. Signals Syst., vol. 2, no. 4, pp. 303-314, 1989.
8. K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural Netw, vol. 4, no. 2, pp. 251-257, 1991.
9. J. Karhunen and J. Joutsensalo, "Generalizations of principal component analysis, optimization problems, neural networks," Neural Netw., vol. 8, no. 4, pp. 549-562, 1995.
10. T. D. Sanger, "Optimal unsupervised learning in a single-layer linear feedforward neural network," Neural Netw., vol. 2, no. 6, pp. 459-473, 1989.
11. C. M. Bishop et al., Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006, vol. 4, no. 4.
12. D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning internal representations by error propagation DTIC Doc., Tech. Rep., 1985.
13. P. J. Werbos, "Beyond regression: New tools for prediction and analysis in the behavioral sciences," Ph.D. dissertation, Harvard Univ., Cambridge, MA, USA, 1974.
14. H. Bourlard and Y. Kamp, "Auto-association by multilayer perceptrons and singular value decomposition," Biol. Cybern., vol. 59, no. 4-5, pp. 291-294, 1988.
15. N. Japkowicz, S. Hanson, M. Gluck, "Nonlinear autoassociation is not equivalent to PCA," Neural Computat., vol. 12, no. 3, pp. 531-545, 2000.
16. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res., vol. 11, pp. 3371-3408, 2010.
17. N. M. Arzeno, Z.-D. Deng, C.-S. Poon, "Analysis of first-derivative based QRS detection algorithms," IEEE Trans. Biomed. Eng., vol. 55, no. 2, pp. 478-484, 2008.
18. P. E. Trahanias, "An approach to QRS complex detection using mathematical morphology," IEEE Trans. Biomed. Eng., vol. 40, no. 2, pp. 201-205, 1993.
19. R. Kohavi et al., "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Int. Joint Conf. Artificial Intelligence, 1995, vol. 14, no. 2, pp. 1137-1145.
20. S. Borra and A. Di Ciaccio, "Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods," Computat. Statist. Data Anal., vol. 54, no. 12, pp. 2976-2989, 2010.
21. M. Saeed et al., "Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database," Crit. Care Med., vol. 39, no. 5, pp. 952-960, 2011.
22. M. Blanco-Velascoa, F. Cruz-Roldána, J. I. Godino-Llorenteb, J. Blanco-Velascoa, C. Armiens-Aparicioa, F. López-Ferreras, "On the use of PRD and CR parameters for ECG compression," Elsevier Med. Eng. Phys., vol. 9, no. 27, pp. 798-802, 2005.