Advances in biological time series prediction by neural networks

Biomedical Signal Processing and Control

Volumn 6, Issue 2, 2011, Pages 112-120

  Panella, Massimo ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Biological time series; Embedding technique; Genetic algorithm; Glucose evolution; Neural networks; Prediction

Indexed keywords

BIOINFORMATICS; BIOLOGY; EMBEDDINGS; FORECASTING; GENETIC ALGORITHMS; GLUCOSE; MOTOR TRANSPORTATION; TIME SERIES;

BIOLOGICAL PARAMETER; DIABETES MELLITUS TYPE 2; EMBEDDING TECHNIQUE; FUNCTION APPROXIMATION PROBLEMS; LOSS OF CONSCIOUSNESS; MIXTURE OF GAUSSIANS; PREDICTION ACCURACY; TIME SERIES PREDICTION;

NEURAL NETWORKS;

GLUCOSE;

ACCURACY; BIOLOGICAL RHYTHM; CONSCIOUSNESS; GENETIC ALGORITHM; HEART RATE; NERVE CELL NETWORK; NON INSULIN DEPENDENT DIABETES MELLITUS; PREDICTION; PRIORITY JOURNAL; REVIEW; RISK ASSESSMENT; SEQUENCE ANALYSIS; SKIN CONDUCTANCE; STRESS;

EID: 79952701895     PISSN: 17468094     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bspc.2010.09.006     Document Type: Article

References (24)

1. F. Masulli, L. Studer, Time series forecasting and neural networks, in: Invited tutorial in Proceedings of IJCNN'99, Washington, DC.
2. I.J. Deary, Effects of hypoglycaemia on cognitive function, in: B.M. Frier, M. Fisher (Eds.), Hypoglycaemia and Diabetes: Clinical and Physiological Aspects, Edward Arnold, London, 1993, pp. 80-92.
3. B. Frier, Driving and diabetes, BMJ 305 (1992) 1238-1239.
4. M. Panella, F. Barcellona, A. Bersani, Neural network in modeling glucose - insulin behavior, in: Biological and Artificial Intelligence Environments, Springer, The Netherlands, 2005, pp. 367-374.
5. H. Abarbanel, Analysis of Observed Chaotic Data, Springer-Verlag Inc., New York, 1996.
6. M. Panella, F. Barcellona, G. Orlandi, Prediction of biological time series by genetic embedding, in: Proceedings of 2009 International Symposium on Bioelectronics & Bioinformatics, Melbourne, Australia, 2009, pp. 41-44.
7. C. Bishop, Neural Networks for Pattern Recognition, Oxford Univ. Press Inc., NY, 1995.
8. M. Panella, A.S. Gallo, An input - output clustering approach to the synthesis of ANFIS networks, IEEE Trans. Fuzzy Syst. 13 (2005) 69-81.
9. Z. Ghahramani, Solving inverse problems using an EM approach to density estimation, in: Proceedings of the 1993 Connectionist Models Summer School, Erlbaum Ass., Hillsdale, NJ, 1994.
10. S. Haykin, J. Principe, Making sense of a complex world, in: IEEE Signal Processing Magazine, 1998, pp. 66-81.
11. A. Tikhonov, V. Arsenin, Solutions of Ill-posed Problems, W.H. Winston Ed., 1977.
12. S. Chen, C. Cowan, P. Grant, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Trans. Neural Network 2 (1991) 302-309.
13. P. Wasserman, Advanced Methods in Neural Computing, Van Nostrand Reinhold, New York, 1993.
14. M. Panella, A. Rizzi, F.M. Frattale Mascioli, G. Martinelli, A constructive EM approach to density estimation for learning, in: Proceedings of IJCNN 2001, Washington, DC, 2001, pp. 2608-2613.
15. M. Panella, A. Rizzi, G. Martinelli, Refining accuracy of environmental data prediction by MoG neural networks, Neurocomputing 55 (2003) 521-549. (Pubitemid 37455473)
16. A. Rizzi, M. Panella, M. Paschero, F.M. Frattale Mascioli, Estimation of bone mineral density data using MoG neural networks, in: Proceedings of IJCNN 2004, Budapest, Hungary, 2004, pp. 3241-3246.
17. G. Grodsky, A threshold distribution hypothesis for packet storage of insulin and its mathematical modeling, J. Clin. Invest. 51 (1972) 2047-2059.
18. J. Guyton, O. Foster, J. Soeldner, M. Tan, C. Kahn, L. Koncz, R. Gleason, Amodel of glucose - insulin homeostasis in man that incorporates the heterogeneous fast pool theory of pancreatic insulin release, Diabetes 27 (1978) 1027-1042. (Pubitemid 9050233)
19. G. Baratta, F. Barcellona, G. Lucidi, A. Bersani, M. Coli, Stability and equilibrium points in MINMOD for glucose, in: Proceedings of 2002 National Congress of SIMAI, 2002.
20. C.D. Man, A. Caumo, C. Cobelli, The oral glucose minimal model: estimation of insulin sensitivity from a meal test, IEEE Trans. Biomembr. Eng. 49 (2002) 419-429. (Pubitemid 34411810)
21. A.D. Gaetano, O. Arino, Mathematical modelling of the intravenous glucose tolerance test, J. Math. Biol. 40 (2000) 136-168.
22. G. Pacini, R. Bergman, MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test, Comput. Methods Programs Biomed. 23 (1986) 113-122.
23. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.
24. G. Boffetta, M. Cencini, M. Falcioni, A. Vulpiani, Predictability: a way to characterize complexity, Phys. Rep. 356 (2002) 367-474.